Texas Instruments AM6x Idagbasoke Multiple kamẹra olumulo Itọsọna

Iboju aabo: Enhances surveillance coverage, object tracking, and recognition accuracy.
Ayika View: Enables stereo vision for tasks like obstacle detection and object manipulation.

Agbohunsile agọ ati Eto Digi kamẹra: Pese agbegbe ti o gbooro ati imukuro awọn aaye afọju.
Aworan Iṣoogun: Offers enhanced precision in surgical navigation and endoscopy.

Drones ati Aworan Aerial: 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.

Akiyesi Ohun elo
Ṣiṣe idagbasoke Awọn ohun elo Kamẹra pupọ lori AM6x

Jianzhong Xu, Qutaiba Saleh

ALÁNṢẸ
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.

Ọrọ Iṣaaju

Awọn kamẹra ti a fi sii ṣe ipa pataki ninu awọn eto iran ode oni. Lilo awọn kamẹra pupọ ninu eto kan faagun awọn agbara ti awọn ọna ṣiṣe ati mu awọn agbara ti ko ṣee ṣe pẹlu kamẹra kan. Isalẹ wa ni diẹ ninu awọn Mofiamples of applications using multiple embedded cameras:

Iboju aabo: Awọn kamẹra pupọ ti a gbe ni ilana pese agbegbe iwo-kakiri okeerẹ. Wọn mu panoramic ṣiṣẹ views, reduce blind spots, and enhance the accuracy of object tracking and recognition, improving overall security measures.
Ayika 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 ati imukuro awọn aaye afọju lati gbogbo awọn ẹgbẹ ti ọkọ ayọkẹlẹ kan.
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. Awọn ẹya ara ẹrọ imotuntun ti AM62A / AM62P SoC ti gbekalẹ ni ọpọlọpọ awọn atẹjade, bii [4], [5], [6], ati bẹbẹ lọ Akọsilẹ ohun elo kii yoo tun ṣe awọn apejuwe ẹya yẹn ṣugbọn dipo idojukọ lori sisọpọ ọpọlọpọ awọn kamẹra CSI-2 sinu awọn ohun elo iran ti a fi sinu AM62A/AM62P.

Tabili 1-1 fihan awọn iyatọ akọkọ laarin AM62A ati AM62P niwọn bi sisẹ aworan jẹ.

Table 1-1. Awọn iyatọ Laarin AM62A ati AM62P ni Ṣiṣe Aworan

SoC	AM62A	AM62P
Ni atilẹyin Iru kamẹra	With or without a built-in ISP	Pẹlu ISP ti a ṣe sinu
Data Ijade kamẹra	Aise/YUV/RGB	YUV/RGB
ISP HWA	Bẹẹni	Rara
Ẹkọ ti o jinlẹ HWA	Bẹẹni	Rara
3-D Graphics HWA	Rara	Bẹẹni

Nsopọ Awọn kamẹra CSI-2 pupọ si SoC
Eto inu Kamẹra lori AM6x SoC ni awọn paati atẹle wọnyi, bi o ṣe han ni Nọmba 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
Ọna kan ti apapọ awọn ṣiṣan kamẹra pupọ ni lati lo ọna ṣiṣe ati deserializing (SerDes) ojutu. Awọn data CSI-2 lati kamẹra kọọkan jẹ iyipada nipasẹ serializer ati gbigbe nipasẹ okun kan. Awọn deserializer gba gbogbo serialized data ti o ti gbe lati awọn kebulu (ọkan USB fun kamẹra), iyipada awọn ṣiṣan pada si CSI-2 data, ati ki o si rán jade ohun interleaved CSI-2 san si awọn nikan CSI-2 RX ni wiwo lori SoC. ṣiṣan kamẹra kọọkan jẹ idanimọ nipasẹ ikanni foju alailẹgbẹ kan. Ojutu ikojọpọ yii nfunni ni anfani afikun ti gbigba asopọ jijin-gigun ti o to 15m lati awọn kamẹra si SoC.

FPD-Link tabi V3-Link serializers ati deserializers (SerDes), ti o ni atilẹyin ni AM6x Linux SDK, jẹ awọn imọ-ẹrọ olokiki julọ fun iru ojutu apapọ CSI-2 yii. Mejeeji awọn FPD-Link ati V3-Link deserializers ni awọn ikanni ẹhin ti o le ṣee lo lati firanṣẹ awọn ifihan agbara amuṣiṣẹpọ fireemu lati muuṣiṣẹpọ gbogbo awọn kamẹra, bi a ti salaye ninu [7].
olusin 2-2 fihan ohun Mofiample ti lilo awọn SerDes lati so awọn kamẹra pupọ pọ si AM6x SoC kan.

An teleample 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
Iru aggregator yii le ni wiwo taara pẹlu awọn kamẹra MIPI CSI-2 pupọ ati ṣajọpọ data lati gbogbo awọn kamẹra si ṣiṣan ṣiṣan CSI-2 kan.

olusin 2-3 fihan ohun Mofiample 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

Muu Awọn kamẹra lọpọlọpọ ṣiṣẹ ni sọfitiwia

Camera Subsystem Software Architecture
olusin 3-1 fihan a ga-ipele Àkọsílẹ aworan atọka ti kamẹra Yaworan eto software ni AM62A/AM62P Linux SDK, bamu si awọn HW eto ni Figure 2-2.

Itumọ sọfitiwia yii jẹ ki SoC gba awọn ṣiṣan kamẹra pupọ pẹlu lilo SerDes, bi o ṣe han ni Nọmba 2-2. FPD-Link/V3-Link SerDes ṣe ipinnu adirẹsi I2C alailẹgbẹ kan ati ikanni foju si kamẹra kọọkan. Ikọja igi ẹrọ alailẹgbẹ yẹ ki o ṣẹda pẹlu adiresi I2C alailẹgbẹ fun gbogbo kamẹra. Awakọ CSI-2 RX mọ kamẹra kọọkan nipa lilo nọmba ikanni foju alailẹgbẹ ati ṣẹda ipo DMA fun ṣiṣan kamẹra. A ṣẹda ipade fidio fun gbogbo ọrọ DMA. Data lati kamẹra kọọkan ti wa ni gbigba ati fipamọ ni lilo DMA si iranti ni ibamu. Awọn ohun elo aaye olumulo lo awọn apa fidio ti o baamu si kamẹra kọọkan lati wọle si data kamẹra. Examples of using this software architecture are given in Chapter 4 – Reference Design.
Eyikeyi awakọ sensọ kan pato ti o ni ibamu pẹlu ilana V4L2 le pulọọgi ati ṣiṣẹ ni faaji yii. Tọkasi [8] nipa bii o ṣe le ṣepọ awakọ sensọ tuntun sinu Linux 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. VPAC (ISP) funrararẹ ni awọn bulọọki pupọ, pẹlu Iha-ọna Aworan Iwoye (VISS), Atunse Distortion Lens (LDC), ati Multiscalar (MSC), kọọkan ti o baamu si ohun itanna GST kan.

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.

Pẹlu ipade fidio ti a ṣẹda fun ọkọọkan awọn kamẹra, opo gigun ti aworan ti o da lori GStreamer ngbanilaaye sisẹ awọn igbewọle kamẹra pupọ (ti a ti sopọ nipasẹ wiwo CSI-2 RX kanna) ni nigbakannaa. Apẹrẹ itọkasi nipa lilo GStreamer fun awọn ohun elo kamẹra pupọ ni a fun ni ori ti nbọ.

Apẹrẹ itọkasi

Ipin yii ṣe afihan apẹrẹ itọkasi ti ṣiṣe awọn ohun elo kamẹra pupọ lori AM62A EVM, lilo Arducam V3Link Camera Solution Kit lati so awọn kamẹra 4 CSI-2 pọ si AM62A ati wiwa ohun elo fun gbogbo awọn kamẹra 4.

Awọn kamẹra atilẹyin
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:

Gẹgẹbi a ti han loke, ohun elo media 30102000.ticsi2rx ni awọn paadi orisun 6, ṣugbọn 4 akọkọ nikan ni a lo, ọkọọkan fun IMX219 kan. Topology paipu media tun le ṣe afihan ni ayaworan. Ṣiṣe aṣẹ atẹle lati ṣe ina aami kan file:

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

Nọmba 4-2 jẹ aworan ti ipilẹṣẹ nipa lilo awọn aṣẹ ti a fun loke. Awọn paati ti o wa ninu faaji sọfitiwia ti Nọmba 3-1 ni a le rii ni aworan yii.

Streaming from Four Cameras
Pẹlu ohun elo mejeeji ati sọfitiwia ti ṣeto daradara, awọn ohun elo kamẹra pupọ le ṣiṣẹ lati aaye olumulo. Fun AM62A, ISP gbọdọ wa ni aifwy lati ṣe agbejade didara aworan to dara. Tọkasi Itọsọna Tuning AM6xA ISP fun bi o ṣe le ṣe atunṣe ISP. Awọn wọnyi ruju bayi examples ti ṣiṣan data kamẹra si ifihan, ṣiṣan data kamẹra si nẹtiwọọki kan, ati fifipamọ data kamẹra si files.

Streaming Camera Data to Display
Ohun elo ipilẹ ti eto kamẹra pupọ ni lati san awọn fidio lati gbogbo awọn kamẹra si ifihan ti o sopọ si SoC kanna. Awọn atẹle jẹ opo gigun ti epo GStreamerample ti ṣiṣan IMX219 mẹrin si ifihan (awọn nọmba node fidio ati awọn nọmba v4l-subdev ninu opo gigun ti epo yoo ṣee yipada lati atunbere si atunbere).

Streaming Camera Data through Ethernet
Dipo ṣiṣanwọle si ifihan ti a ti sopọ si SoC kanna, data kamẹra le tun ṣe ṣiṣan nipasẹ Ethernet. Ẹgbẹ gbigba le jẹ boya ero isise AM62A/AM62P miiran tabi PC agbalejo. Awọn atẹle jẹ ẹya example ti ṣiṣan data kamẹra nipasẹ Ethernet (lilo awọn kamẹra meji fun ayedero) (ṣe akiyesi ohun itanna encoder ti a lo ninu opo gigun ti epo):

Awọn atẹle jẹ ẹya example ti gbigba data kamẹra ati ṣiṣanwọle si ifihan lori ero isise AM62A/AM62P miiran:

Storing Camera Data to Files
Instead of streaming to a display or through a network, the camera data can be stored in local files. Opo gigun ti o wa ni isalẹ tọju data kamẹra kọọkan si a file (lilo awọn kamẹra meji bi example fun ayedero).

Multicamera Deep Learning Inference

AM62A ti ni ipese pẹlu imuyara ẹkọ ti o jinlẹ (C7x-MMA) pẹlu to TOPS meji, eyiti o lagbara lati ṣiṣẹ awọn oriṣi awọn awoṣe ikẹkọ jinlẹ fun isọdi, wiwa ohun, ipin atunmọ, ati diẹ sii. Abala yii fihan bi AM62A ṣe le ṣiṣẹ awọn awoṣe ikẹkọ jinlẹ mẹrin ni nigbakannaa lori awọn kikọ sii kamẹra mẹrin ti o yatọ.

Aṣayan awoṣe
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.

Table 4-1. Ṣe afihan Awọn ẹya ara ẹrọ ti Awoṣe TFL-OD-2000-ssd-mobV1-coco-mlperf.

Awoṣe	Iṣẹ-ṣiṣe	Ipinnu	FPS	mAP 50% Accuracy On COCO	Lairi/Fireemu (ms)	DDR BW Utilization (MB/ Frame)
TFL-OD-2000-ssd- mobV1-koko-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 ti o gba laaye piparẹ diẹ ninu sisẹ media ati itọkasi ikẹkọ jinlẹ si awọn ohun elo accelerators. Diẹ ninu awọn examples ti awọn wọnyi plugins pẹlu tiovxisp, tiovxmultiscaler, tiovxmosaic, ati tidlinferer. Opo gigun ti epo ni Nọmba 4-3 pẹlu gbogbo ohun ti a beere plugins for a multipath GStreamer pipeline for 4-camera inputs, each with media preprocess, deep learning inference, and postprocess. The duplicated plugins fun kọọkan awọn ọna kamẹra ti wa ni tolera ni awonya fun rọrun ifihan.
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.

Nigbamii ni iwe afọwọkọ opo gigun ti kikun fun ọran lilo ikẹkọ jinlẹ pupọ kamẹra ti o han ni Nọmba 4-3.

Performance Analysis

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.

Gẹgẹbi a ti han tẹlẹ ni Nọmba 4-4, opo gigun ti ẹkọ ti o jinlẹ nlo ohun itanna tiperfoverlay GStreamer lati ṣafihan awọn ẹru mojuto Sipiyu bi ayaworan igi ni isalẹ iboju naa. Nipa aiyipada, aworan naa ti ni imudojuiwọn ni gbogbo iṣẹju-aaya meji lati ṣafihan awọn ẹru bi ogorun lilotage. 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.

Table 5-1. Iṣe (FPS) ati Lilo Awọn orisun AM62A nigba lilo pẹlu Awọn kamẹra 4 IMX219 fun Ifihan iboju, ṣiṣan Ethernet, Gba silẹ si Files, ati Ṣiṣe Itọkasi Ẹkọ Jin

Ohun elo n	Pipeline (operation )	Abajade	FPS avg pipeline s	FPS lapapọ	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]
Ko si App	Baseline No operation	NA	NA	NA	1.87	1	0	0	0	0	560	19	579
Kamẹra nikan	ṣiṣanwọle to Screen	Iboju	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
	Gba silẹ si files	4 files 1920×1080	30	120	25	3	0	70	0	0	2100	1403	3503
Kamẹra with Deep learning	Deep learning: Object detection MobV1- coco	Iboju	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

Lakotan
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, fi ohun ibeere ni TI E2E forum.

Awọn itọkasi

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

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Q: Ṣe MO le lo iru kamẹra eyikeyi pẹlu idile AM6x ti awọn ẹrọ?

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 Idagbasoke Multiple Kamẹra [pdf] Itọsọna olumulo
AM62A, AM62P, AM6x Idagbasoke Kamẹra Ọpọ, AM6x, Idagbasoke Kamẹra Ọpọ, Kamẹra Ọpọ, Kamẹra

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Texas Instruments AM6x Developing Multiple Cameras

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