Texas Instruments AM6x Ukuphuhlisa isiKhokelo soMsebenzisi weekhamera ezininzi

Uvavanyo loKhuseleko: Enhances surveillance coverage, object tracking, and recognition accuracy.
Jikeleza View: Enables stereo vision for tasks like obstacle detection and object manipulation.

Isishicileli seKhabhinethi kunye neNkqubo yeMirror yeKhamera: Ibonelela ngokhuseleko olwandisiweyo kwaye ishenxise iindawo ezingaboniyo.
Umfanekiso woNyango: Offers enhanced precision in surgical navigation and endoscopy.

Iidrone kunye nomfanekiso wasemoyeni: Capture high-resolution images from different angles for various applications.

Connecting Multiple CSI-2 Cameras to the SoC:
To connect multiple CSI-2 cameras to the SoC, follow the guidelines provided in the user manual. Ensure proper alignment and connection of each camera to the designated ports on the SoC.

Inqaku leSicelo
Ukuphuhliswa kwezicelo zeekhamera ezininzi kwi-AM6x

Jianzhong Xu, Qutaiba Saleh

UMXHOLO
This report describes application development using multiple CSI-2 cameras on the AM6x family of devices. A reference design of object detection with deep learning on 4 cameras on the AM62A SoC is presented with performance analysis. General principles of the design apply to other SoCs with a CSI-2 interface, such as AM62x and AM62P.

Intshayelelo

Iikhamera ezifakelweyo zidlala indima ebalulekileyo kwiinkqubo zombono zale mihla. Ukusebenzisa iikhamera ezininzi kwinkqubo kwandisa amandla ezi nkqubo kwaye kwenza amandla angenakwenzeka ngekhamera enye. Apha ngezantsi kukho examples of applications using multiple embedded cameras:

ULungelo loKhuseleko: Iikhamera ezininzi ezibekwe ngobuchule zibonelela ngokhuselo olubanzi. Bavumela i-panoramic views, reduce blind spots, and enhance the accuracy of object tracking and recognition, improving overall security measures.
Jikeleza View: Multiple cameras are used to create a stereo vision setup, enabling three-dimensional information and the estimation of depth. This is crucial for tasks such as obstacle detection in autonomous vehicles, precise object manipulation in robotics, and enhanced realism of augmented reality experiences.

Cabin Recorder and Camera Mirror System: A car cabin recorder with multiple cameras can provide more coverage using a single processor. Similarly, a camera mirror system with two or more cameras can expand the driver’s field of view kunye nokuphelisa iindawo ezingaboniyo kuwo onke macala emoto.
Medical Imaging: Multiple cameras can be used in medical imaging for tasks like surgical navigation, providing surgeons with multiple perspectives for enhanced precision. In endoscopy, multiple cameras enable a thorough examination of internal organs.

Drones and Aerial Imaging: Drones often come equipped with multiple cameras to capture high-resolution images or videos from different angles. This is useful in applications like aerial photography, agriculture monitoring, and land surveying.
With the advancement of microprocessors, multiple cameras can be integrated into a single System-on-Chip.
(SoC) to provide compact and efficient solutions. The AM62Ax SoC, with high-performance video/vision processing and deep learning acceleration, is an ideal device for the above-mentioned use cases. Another AM6x device, the AM62P, is built for high-performance embedded 3D display applications. Equipped with 3D graphics acceleration, the AM62P can easily stitch together the images from multiple cameras and produce a high-resolution panoramic view. Iimpawu ezintsha ze-AM62A/AM62P SoC zinikezelwe kwiimpapasho ezahlukeneyo, ezifana [4], [5], [6], njl. Eli nqaku lesicelo aliyi kuphinda le nkcazo yempawu kodwa endaweni yoko lijolise ekudibaniseni iikhamera ezininzi ze-CSI-2 kwizicelo zombono ezifakwe kwi-AM62A/AM62P.

Itheyibhile 1-1 ibonisa umahluko ophambili phakathi kwe-AM62A kunye ne-AM62P ngokubhekiselele ekuqhutyweni komfanekiso.

Uluhlu 1-1. Umahluko phakathi kwe-AM62A kunye ne-AM62P kwiNkqubo yoMfanekiso

I-SoC	AM62A	I-AM62P
Uhlobo lweKhamera exhaswayo	With or without a built-in ISP	Nge-ISP eyakhelwe-ngaphakathi
Idatha yokuPhuma kweKhamera	Ekrwada/YUV/RGB	YUV/RGB
ISP HWA	Ewe	Hayi
Ukufunda nzulu HWA	Ewe	Hayi
Imizobo ye-3-D HWA	Hayi	Ewe

Ukuqhagamshela iiCamera ezininzi ze-CSI-2 kwi-SoC
Isixokelelwano seKhamera kwi-AM6x SoC iqulethe la macandelo alandelayo, njengoko kubonisiwe kuMfanekiso 2-1:

MIPI D-PHY Receiver: receives video streams from external cameras, supporting up to 1.5 Gbps per data lane for 4 lanes.

CSI-2 Receiver (RX): receives video streams from the D-PHY receiver and either directly sends the streams to the ISP or dumps the data to DDR memory. This module supports up to 16 virtual channels.
SHIM: a DMA wrapper that enables sending the captured streams to memory over DMA. Multiple DMA contexts can be created by this wrapper, with each context corresponding to a virtual channel of the CSI-2 Receiver.

Multiple cameras can be supported on the AM6x through the use of virtual channels of CSI-2 RX, even though there is only one CSI-2 RX interface on the SoC. An external CSI-2 aggregating component is needed to combine multiple camera streams and send them to a single SoC. Two types of CSI-2 aggregating solutions can be used, described in the following sections.

CSI-2 Aggregator Using SerDes
Enye indlela yokudibanisa imijelo yeekhamera ezininzi kukusebenzisa isisombululo se-serializing kunye ne-deserializing (SerDes). Idatha ye-CSI-2 kwikhamera nganye iguqulwa yi-serializer kwaye idluliselwe ngekhebula. I-deserializer ifumana yonke idatha ye-serialized edluliselwe kwiintambo (intambo enye ngekhamera), iguqule imilambo ibuyele kwidatha ye-CSI-2, ize ithumele umlambo we-CSI-2 odibeneyo kwi-interface ye-CSI-2 RX eyodwa kwi-SoC. Umjelo ngamnye wekhamera ichongiwe ngumjelo okhethekileyo wenyani. Esi sisombululo sokudibanisa sinika inzuzo eyongezelelweyo yokuvumela uxhulumaniso olude olude ukuya kwi-15m ukusuka kwiikhamera ukuya kwi-SoC.

I-FPD-Link okanye i-V3-Link serializers kunye ne-deserializers (i-SerDes), exhaswa kwi-AM6x ye-Linux SDK, zezona teknoloji zidumileyo zolu hlobo lwesisombululo se-CSI-2 sokudibanisa. Zombini i-FPD-Link kunye ne-V3-Link deserializers zineziteshi ezingasemva ezingasetyenziselwa ukuthumela iimpawu zokuvumelanisa isakhelo ukuvumelanisa zonke iikhamera, njengoko kuchaziwe kwi- [7].
Umfanekiso 2-2 ubonisa i-example lokusebenzisa i-SerDes ukudibanisa iikhamera ezininzi kwi-AM6x ye-SoC enye.

Umdalaample of this aggregating solution can be found in the Arducam V3Link Camera Solution Kit. This kit has a deserializer hub which aggregates 4 CSI-2 camera streams, as well as 4 pairs of V3link serializers and IMX219 cameras, including FAKRA coaxial cables and 22-pin FPC cables. The reference design discussed later is built on this kit.

CSI-2 Aggregator without Using SerDes
Olu hlobo lwe-aggregator lunokunxibelelana ngokuthe ngqo kunye neekhamera ezininzi ze-MIPI CSI-2 kunye nokudibanisa idatha ukusuka kuzo zonke iikhamera ukuya kumjelo owodwa wokuphuma kwe-CSI-2.

Umfanekiso 2-3 ubonisa i-example of such a system. This type of aggregating solution does not use any serializer/deserializer but is limited by the maximum distance of CSI-2 data transfer, which is up to 30cm. The AM6x Linux SDK does not support this type of CSI-2 aggregator

Ukunika amandla iikhamera ezininzi kwiSoftware

Camera Subsystem Software Architecture
Umzobo we-3-1 ubonisa umzobo webhloko ephezulu ye-software yokubamba ikhamera kwi-AM62A / AM62P Linux SDK, ehambelana nenkqubo ye-HW kwi-Figure 2-2.

Olu lwakhiwo lwesoftware lwenza ukuba i-SoC ifumane imijelo yeekhamera ezininzi ngokusetyenziswa kweSerDes, njengoko kubonisiwe kuMfanekiso 2-2. I-FPD-Link/V3-Link SerDes inika idilesi ye-I2C ekhethekileyo kunye neshaneli ebonakalayo kwikhamera nganye. Isixhobo esikhethekileyo somthi walekayo kufuneka senziwe ngedilesi ye-I2C ekhethekileyo yekhamera nganye. Umqhubi we-CSI-2 RX uqaphela ikhamera nganye usebenzisa inombolo yetshaneli ekhethekileyo kwaye wenze umxholo we-DMA ngekhamera nganye. Indawo yevidiyo yenzelwe yonke imeko ye-DMA. Idatha esuka kwikhamera nganye ifunyenwe kwaye igcinwe kusetyenziswa i-DMA kwimemori ngokufanelekileyo. Usetyenziso lwesithuba somsebenzisi zisebenzisa iinowudi zevidiyo ezihambelana nekhamera nganye ukufikelela kwidatha yekhamera. Eksamples of using this software architecture are given in Chapter 4 – Reference Design.
Nawuphi na umqhubi wenzwa ethile ohambelana nesakhelo seV4L2 unokuplaga kwaye adlale kolu lwakhiwo. Jonga ku [8] malunga nendlela yokudibanisa umqhubi woluvo omtsha kwiLinux SDK.

Image Pipeline Software Architecture

The AM6x Linux SDK provides the GStreamer (GST) framework, which can be used in the ser space to integrate the image processing components for various applications. The Hardware Accelerators (HWA) on the SoC, such as the Vision Pre-processing Accelerator (VPAC) or ISP, video encoder/decoder, and deep learning compute engine, are accessed through GST plugins. I-VPAC (ISP) ngokwayo ineebhloko ezininzi, kuquka i-Vision Imaging Sub-System (VISS), i-Lens Distortion Correction (LDC), kunye ne-Multiscalar (MSC), nganye ehambelana ne-plugin ye-GST.

Figure 3-2 shows the block diagram of a typical image pipeline from the camera to encoding or deep
learning applications on AM62A. For more details about the end-to-end data flow, refer to the EdgeAI SDK documentation.

For AM62P, the image pipeline is simpler because there is no ISP on AM62P.

Ngomfanekiso wevidiyo owenziwe kwikhamera nganye, umbhobho womfanekiso osekelwe kwi-GStreamer uvumela ukucutshungulwa kwamagalelo amaninzi ekhamera (edityaniswe nge-interface efanayo ye-CSI-2 RX) ngaxeshanye. Uyilo lwereferensi olusebenzisa i-GStreamer kusetyenziso lweekhamera ezininzi lunikiwe kwisahluko esilandelayo.

Uyilo lweReferensi

Esi sahluko sibonisa ireferensi yoyilo lokuqhuba izicelo zeekhamera ezininzi kwi-AM62A EVM, usebenzisa i-Arducam V3Link Camera Solution Kit ukuxhuma iikhamera ze-4 CSI-2 kwi-AM62A kunye nokuqhuba ukufunyanwa kwezinto kuzo zonke iikhamera ze-4.

Iikhamera ezixhaswayo
The Arducam V3Link kit works with both FPD-Link/V3-Link-based cameras and Raspberry Pi-compatible CSI-2 cameras. The following cameras have been tested:

D3 Engineering D3RCM-IMX390-953
Leopard Imaging LI-OV2312-FPDLINKIII-110H

IMX219 cameras in the Arducam V3Link Camera Solution Kit

Setting up Four IMX219 Cameras
Follow the instructions provided in the AM62A Starter Kit EVM Quick Start Guide to set up the SK-AM62A-LP EVM (AM62A SK) and ArduCam V3Link Camera Solution Quick Start Guide to connect the cameras to AM62A SK through the V3Link kit. Make sure the pins on the flex cables, cameras, V3Link board, and AM62A SK are all aligned properly.

Figure 4-1 shows the setup used for the reference design in this report. The main components in the setup include:

1X SK-AM62A-LP EVM board
1X Arducam V3Link d-ch adapter board
FPC cable connecting Arducam V3Link to SK-AM62A

4X V3Link camera adapters (serializers)
4X RF coaxial cables to connect V3Link serializers to V3Link d-ch kit
4X IMX219 Cameras

4X CSI-2 22-pin cables to connect cameras to serializers
Cables: HDMI cable, USB-C to power SK-AM62A-LP and 12V power sourced for V3Link d-ch kit)
Other components not shown in Figure 4-1: micro-SD card, micro-USB cable to access SK-AM62A-LP, and Ethernet for streaming

Configuring Cameras and CSI-2 RX Interface
Set up the software according to the instructions provided in the Arducam V3Link Quick Start Guide. After running the camera setup script, setup-imx219.sh, the camera’s format, the CSI-2 RX interface format, and the routes from each camera to the corresponding video node will be configured properly. Four video nodes are created for the four IMX219 cameras. Command “v4l2-ctl –list-devices” displays all the V4L2 video devices, as shown below:

There are 6 video nodes and 1 media node under tiscsi2rx. Each video node corresponds to a DMA context allocated by the CSI2 RX driver. Out of the 6 video nodes, 4 are used for the 4 IMX219 cameras, as shown in the media pipe topology below:

Njengoko kubonisiwe ngasentla, i-media entity 30102000.ticsi2rx ine-pads yemithombo ye-6, kodwa kuphela i-4 yokuqala esetyenziswayo, nganye kwi-IMX219 enye. I-Topology yemibhobho yemidiya nayo inokuzotywa ngokomzobo. Sebenzisa lo myalelo ulandelayo ukuvelisa ichaphaza file:

Then run the command below on a Linux host PC to generate a PNG file:

Umzobo 4-2 ngumfanekiso owenziwe kusetyenziswa imiyalelo enikwe ngasentla. Amacandelo kuyilo lwesoftware yoMfanekiso 3-1 anokufumaneka kule grafu.

Streaming from Four Cameras
Ngezo zombini ihardware kunye nesoftware zisekwe ngokufanelekileyo, usetyenziso lweekhamera ezininzi zinokusebenza ukusuka kwindawo yomsebenzisi. Kwi-AM62A, i-ISP kufuneka ilungiswe ukuvelisa umgangatho womfanekiso omhle. Jonga kwi-AM6xA ISP Tuning Isikhokelo ngendlela yokwenza i-ISP tuning. La macandelo alandelayo abonisa exampUkusasazwa kwedatha yekhamera kwisiboniso, ukusasaza idatha yekhamera kwinethiwekhi, kunye nokugcina idatha yekhamera files.

Streaming Camera Data to Display
Usetyenziso olusisiseko lwale nkqubo yeekhamera ezininzi kukusasaza iividiyo ukusuka kuzo zonke iikhamera ukuya kumboniso oqhagamshelwe kwiSoC efanayo. Oku kulandelayo ngumbhobho weGStreamer example yokusasaza i-IMX219 emine ukuya kumboniso (amanani eendawo zevidiyo kunye namanani e-v4l-subdev kumbhobho anokutshintsha ukusuka ekuqaliseni kwakhona ukuya ekuqaliseni ngokutsha).

Streaming Camera Data through Ethernet
Esikhundleni sokusasaza kwisiboniso esiqhagamshelwe kwi-SoC efanayo, idatha yekhamera inokusasazwa nge-Ethernet. Icala elifumanayo linokuba yenye iprosesa ye-AM62A/AM62P okanye iPC yomkhosi. Oku kulandelayo yi-exampukusasaza idatha yekhamera nge-Ethernet (usebenzisa iikhamera ezimbini ukwenza lula) (qaphela iplagin ye-encoder esetyenziswe kumbhobho):

Oku kulandelayo yi-example yokufumana idatha yekhamera kunye nokusasazwa kwisiboniso kwenye iprosesa ye-AM62A/AM62P:

Storing Camera Data to Files
Instead of streaming to a display or through a network, the camera data can be stored in local files. Umbhobho ongezantsi ugcina idatha yekhamera nganye ku file (usebenzisa iikhamera ezimbini njenge-example ukwenza lula).

Multicamera Deep Learning Inference

I-AM62A ixhotyiswe nge-accelerator yokufunda enzulu (C7x-MMA) ukuya kuthi ga kwi-TOPS ezimbini, ezikwaziyo ukuqhuba iindidi ngeendidi zemifuziselo yokufunda enzulu yokuhlelwa, ukufumanisa into, ulwahlulo lwe-semantic, kunye nokunye. Eli candelo libonisa indlela i-AM62A enokuthi ngaxeshanye iqhube ngayo iimodeli ezine ezinzulu zokufunda kwiifidi ezine zeekhamera ezahlukeneyo.

Ukukhetha imodeli
The TI’s EdgeAI-ModelZoo provides hundreds of state-of-the-art models, which are converted/exported from their original training frameworks to an anembedded-friendlyy format so that they can be offloaded to the C7x-MMA deep learning accelerator. The cloud-based Edge AI Studio Model Analyzer provides an easy-to-use “Model Selection” tool. It is dynamically updated to include all models supported in TI EdgeAI-ModelZoo. The tool requires no previous experience and provides an easy-to-use interface to enter the features required in the desired model.

The TFL-OD-2000-ssd-mobV1-coco-mlperf was selected for this multi-camera deep learning experiment. This multi-object detection model is developed in the TensorFlow framework with a 300×300 input resolution. Table 4-1 shows the important features of this model when trained on the cCOCO dataset with about 80 different classes.

Uluhlu 4-1. Iimpawu eziphambili zeModeli ye-TFL-OD-2000-ssd-mobV1-coco-mlperf.

Umzekelo	Umsebenzi	Isigqibo	I-FPS	mAP 50% Accuracy On COCO	Ukubambezeleka/Isakhelo (ms)	DDR BW Utilization (MB/ Frame)
TFL-OD-2000-ssd- mobV1-coco-mlperf	Multi Object Detection	300×300	~152	15.9	6.5	18.839

Pipeline Setup
Figure 4-3 shows the 4-camera deep learning GStreamer pipeline. TI provides a suite of GStreamer plugins evumela ukukhuphela enye yenkqubo yemidiya kunye nentekelelo yokufunda enzulu kwii-accelerator zehardware. Abanye exampkancinci kwezi plugins ziquka i-tiovxisp, i-tiovxmultiscaler, i-tiovxmosaic, kunye ne-tidlinferer. Umbhobho kuMfanekiso 4-3 ubandakanya zonke ezifunekayo plugins for a multipath GStreamer pipeline for 4-camera inputs, each with media preprocess, deep learning inference, and postprocess. The duplicated plugins kwindlela nganye yekhamera zipakishwe kwigrafu ukwenzela umboniso olula.
The available hardware resources are evenly distributed among the four camera paths. For instance, AM62A contains two image multiscalers: MSC0 and MSC1. The pipeline explicitly dedicates MSC0 to process camera 1 and camera 2 paths, while MSC1 is dedicated to camera 3 and camera 4.

The output of the four camera pipelines is scaled down and concatenated together using the tiovxmosaic plugin. The output is displayed on a single screen. Figure 4-4 shows the output of the four cameras with a deep learning model running object detection. Each pipeline (camera) is running at 30 FPS and a total of 120 FPS.

Okulandelayo siskripthi sombhobho esipheleleyo sosetyenziso olunzulu lweekhamera ezininzi eziboniswe kuMfanekiso 4-3.

Uhlalutyo lweNtsebenzo

The setup with four cameras using the V3Link board and the AM62A SK was tested in various application scenarios, including directly displaying on a screen, streaming over Ethernet (four UDP channels), recording to 4 separate files, and with deep learning inference. In each experiment, we monitored the frame rate and the utilization of CPU cores to explore the whole system’s capabilities.

Njengoko kubonisiwe ngaphambili kuMfanekiso 4-4, umbhobho wokufunda nzulu usebenzisa iplagi ye-tiperfoverlay GStreamer ukubonisa imithwalo engundoqo ye-CPU njengegrafu yebha ezantsi kwesikrini. Ngokungagqibekanga, igrafu ihlaziywa rhoqo kwimizuzwana emibini ukubonisa imithwalo njengepesenti yokusetyenziswatage. In addition to the tiperfoverlay GStreamer plugin, the perf_stats tool is a second option to show core performance directly on the terminal with an option for saving to a file. This tool is more accurate compared to the tTiperfoverlayas the latter adds extra load on theARMm cores and the DDR to draw the graph and overlay it on the screen. The perf_stats tool is mainly used to collect hardware utilization results in all of the test cases shown in this document. Some of the important processing cores and accelerators studied in these tests include the main processors (four A53 Arm cores @ 1.25GHz), the deep learning accelerator (C7x-MMA @ 850MHz), the VPAC (ISP) with VISS and multiscalers (MSC0 and MSC1), and DDR operations.

Table 5-1 shows the performance and resource utilization when using AM62A with four cameras for three use cases, including streaming four cameras to a display, streaming over Ethernet, and recording to four separate files. Two tests are implemented in each use case: with the camera only and with deep learning inference. In addition, the first row in Table 5-1 shows hardware utilizations when only the operating system was running on AM62A without any user applications. This is used as a baseline to compare against when evaluating hardware utilizations of the other test cases. As shown in the table, the four cameras with deep learning and screen display operated at 30 FPS each ,with a total of 120 FPS for the four cameras. This high frame rate is achieved with only 86% of the deep learning accelerator (C7x-MMA) full capacity. In addition, it is important to note that the deep learning accelerator was clocked at 850MHz instead of 1000MHz in these experiments, which is about only 85% of its maximum performance.

Itheyibhile 5-1. Ukusebenza (i-FPS) kunye nokuSetyenziswa kweZibonelelo ze-AM62A xa zisetyenziswa kunye neeCamera ze-4 IMX219 zokubonisa iSkrini, i-Ethernet Stream, Rekhoda ukuya Files, kunye nokwenza uPhando lokuFundisa ngokuNzulu

Usetyenziso n	Pipeline (operation )	Isiphumo	I-FPS avg pipeline s	I-FPS iyonke	MPUs A53s @ 1.25 GHz [%]	MCU R5 [%]	DLA (C7x- MMA) @ 850 MHz [%]	VISS [%]	MSC0 [%]	MSC1 [%]	DDR Rd [MB/s]	DDR Wr [MB/s]	DDR Total [MB/s]
Akukho App	Baseline No operation	NA	NA	NA	1.87	1	0	0	0	0	560	19	579
Ikhamera kuphela	Umsinga to Screen	Ikhusi	30	120	12	12	0	70	61	60	1015	757	1782
	Stream over Ethernet	UDP: 4 ports 1920×1080	30	120	23	6	0	70	0	0	2071	1390	3461
	Rekhoda ukuya files	4 files 1920×1080	30	120	25	3	0	70	0	0	2100	1403	3503
Cam with Deep learning	Deep learning: Object detection MobV1- coco	Ikhusi	30	120	38	25	86	71	85	82	2926	1676	4602
	Deep learning: Object detection MobV1- coco and Stream over Ethernet	UDP: 4 ports 1920×1080	28	112	84	20	99	66	65	72	4157	2563	6720
	Deep learning: Object detection MobV1- coco and record to files	4 files 1920×1080	28	112	87	22	98	75	82	61	2024	2458	6482

Isishwankathelo
This application report describes how to implement multi-camera applications on the AM6x family of devices. A reference design based on Arducam’s V3Link Camera Solution Kit and AM62A SK EVM is provided in the report, with several camera applications using four IMX219 cameras, such as streaming and object detection. Users are encouraged to acquire the V3Link Camera Solution Kit from Arducam and replicate these examples. The report also provides a detailed analysis of the performance of AM62A while using four cameras under various configurations, including displaying to a screen, streaming over Ethernet, and recording to files. It also showsAM62A’sA capability of performing deep learning inference on four separate camera streams in parallel. If there are any questions about running these exampLes, ngenisa umbuzo kwiforum ye-TI E2E.

Iimbekiselo

AM62A Starter Kit EVM Quick Start Guide

ArduCam V3Link Camera Solution Quick Start Guide
Edge AI SDK documentation for AM62A
Edge AI Smart Cameras Using Energy-Efficient AM62A Processor

Camera Mirror Systems on AM62A
Driver and Occupancy Monitoring Systems on AM62A
Quad Channel Camera Application for Surround View and CMS Camera Systems

AM62Ax Linux Academy on Enabling CIS-2 Sensor
Edge AI ModelZoo
Edge AI Studio

Perf_stats tool

TI Parts Referred in This Application Note:

https://www.ti.com/product/AM62A7

ISAZISO ESIBALULEKILEYO KUNYE NENKCAZELO

I-TI IBONISA IINKCUKACHA ZOBUGCISA NOKUTHEMBEKILEYO (KUQUKA I-DATA SHEETS), IZIXHOBO ZOKUYILWA (KUHLANGANISA IIREferensi Uyilo), ISICELO OKANYE ESINYE IINGCEBISO ZOYILWA, WEB IZIXHOBO, IINKCUKACHA ZOKHUSELEKO, KUNYE NEZINYE IZIXHOBO “NJENGOKO ZINJALO” KUNYE NAZO ZONKE Iziphoso, KUNYE NEZIBAKALO ZONKE, IZIQINISEKISO, ZICHAZEKILEYO NEZITHETHEKILEYO, KUHLANGANISA NGAPHANDLE KOMDA NAZIPHI NA IZIQINISEKISO EZICHAPHAZELEKILEYO ZOKURHWETHWA, UKULUNGA NGENXA YESITHATHU INGXENYE ENGQEQESHE. AMALUNGELO EPROPATI .

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ukukhetha iimveliso ze-TI ezifanelekileyo kwisicelo sakho,

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Imibuzo ebuzwa qho

Umbuzo: Ngaba ndingasebenzisa naluphi na uhlobo lwekhamera kunye nosapho lwe-AM6x lwezixhobo?

The AM6x family supports different camera types, including those with or without built-in ISP. Refer to the specifications for more details on supported camera types.

: What are the main differences between AM62A and AM62P in image processing?

The key variations include supported camera types, camera output data, presence of ISP HWA, Deep Learning HWA, and 3-D Graphics HWA. Refer to the specifications section for a detailed comparison.

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Texas Instruments AM6x Ukuphuhlisa iikhamera ezininzi [pdf] Isikhokelo somsebenzisi
I-AM62A, i-AM62P, i-AM6x iPhuhlisa iiKhamera ezininzi, i-AM6x, iPhuhlisa iiKhamera ezininzi, iiKhamera ezininzi, iKhamera

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Texas Instruments AM6x Developing Multiple Cameras

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