Object Detection on i.mx8m Plus Smart Board

Object Detection using iWave's i.mx8m Plus Smart Board

Object detection is a cutting-edge computer technology that deals with detecting instances of semantic objects within digital and physical images, as well as videos. This innovative field combines computer vision and image processing to identify objects such as humans, buildings, cars, and more.

The AAML Platform

The AAML (Advanced Analytics and Machine Learning) platform is a comprehensive solution that enables object detection in real-time. The platform consists of three primary components: a USB camera, the i.mx8m Plus Smart Development Platform, and a monitor.

  • USB Camera: The MyPCSI-USB camera feeds live images to the i.mx8m Plus platform, providing real-time data for object detection.
  • i.mx8m Plus Smart Development Platform: This platform processes the live image stream from the USB camera and utilizes an NPU (Neural Processing Unit) to analyze the data.
  • Monitor: The detected objects are displayed in real-time on a monitor, providing visual feedback of the object detection process.

Image Classification and Object Detection

The i.mx8m Plus board is integrated with the NXPE CopenCV machine learning software, which enables object detection in real-time. The software assigns labels to detected objects, providing accurate classification.

  • Image Classifier: The NPU utilizes an image classifier to identify the presence of specific images within the live stream.
  • Object Detection Algorithm: The NXPE CopenCV software detects a variety of objects, assigning labels and providing accurate classification in real-time.

Enhanced Identification using Pi-AQ

The use of Pi-AQ with different inference engines and algorithms enhances the object detection process, making identification more efficient and accurate.

  • Multi-Engine Support: Pi-AQ supports multiple inference engines, enabling flexibility in choosing the most suitable algorithm for specific object detection tasks.
  • Algorithm Selection: The ability to select from various algorithms enables optimized performance and accuracy for diverse object detection applications.

Object Detection in Captured Images

Object detection can also be performed on already captured images, providing the ability to analyze and classify objects within static images.

  • Offline Analysis: Object detection in captured images enables offline analysis, allowing for the examination of objects without requiring a live feed.
  • Static Image Classification: The software can classify and label objects within static images, providing accurate classification and identification.

**Object Detection** Object detection is a fundamental problem in computer vision that deals with locating and classifying objects within an image or video.
**Background** The goal of object detection is to identify the location, size, and category of all objects in an image. This task has been a cornerstone of computer vision research for decades, with applications in various fields such as robotics, surveillance, autonomous vehicles, and medical imaging.
**History** The concept of object detection dates back to the early days of computer vision. One of the first approaches was the " Template Matching" method proposed by Rosenfeld in 1969. Since then, various techniques have been developed, including edge detection, feature extraction, and sliding window methods.
**Deep Learning Era** The advent of deep learning has revolutionized the field of object detection. In 2014, Girshick et al. proposed R-CNN (Region-based Convolutional Neural Networks), which used a combination of convolutional neural networks (CNNs) and region proposal algorithms to detect objects. This was followed by Faster R-CNN, YOLO (You Only Look Once), and SSD (Single Shot Detector), each improving the accuracy and speed of object detection.

Introduction
The i.MX8M Plus Smart Board is a powerful single-board computer designed for IoT and AI applications. One of the key features of this board is its ability to perform object detection, which enables it to identify and classify objects within images or video streams. In this article, we will explore the details of object detection on the i.MX8M Plus Smart Board.

Hardware Specifications
The i.MX8M Plus Smart Board is powered by a quad-core ARM Cortex-A53 processor, which provides the necessary processing power for demanding AI workloads. Additionally, it features a dedicated Neural Processing Unit (NPU) that accelerates machine learning tasks such as object detection.

Software Frameworks
The i.MX8M Plus Smart Board supports various software frameworks for object detection, including:
  • TensorFlow Lite: A lightweight version of the popular TensorFlow framework, optimized for embedded systems.
  • OpenCV: A computer vision library that provides pre-trained models and tools for object detection.
  • Caffe: A deep learning framework that enables fast prototyping and deployment of AI models.

Object Detection Algorithms
The i.MX8M Plus Smart Board supports various object detection algorithms, including:
  • Yolo (You Only Look Once): A real-time object detection algorithm that detects objects in one pass.
  • SSD (Single Shot Detector): A fast and accurate object detection algorithm that uses a single neural network to detect objects.
  • Faster R-CNN: A region-based object detection algorithm that uses a two-stage approach to detect objects.

Performance
The i.MX8M Plus Smart Board provides high-performance object detection capabilities, with the ability to process up to 1080p video streams at 30 FPS. Additionally, it features a low power consumption of around 5W, making it suitable for battery-powered applications.

Use Cases
The i.MX8M Plus Smart Board is suitable for various use cases that require object detection, including:
  • Smart Surveillance: The board can be used to build smart surveillance systems that detect and track objects in real-time.
  • Industrial Automation: The board can be used to build industrial automation systems that detect and classify objects on a production line.
  • Robotics: The board can be used to build robots that detect and interact with objects in their environment.

Q1: What is object detection on i.MX8M Plus Smart Board? The i.MX8M Plus Smart Board is a single-board computer that supports object detection, which is the ability to detect and classify objects within images or video streams.
Q2: What are the key features of the i.MX8M Plus processor for object detection? The i.MX8M Plus processor features a quad-core Cortex-A53 CPU, a Cortex-M4 microcontroller, and a dedicated Vision Processing Unit (VPU) that accelerates computer vision tasks like object detection.
Q3: What are some popular deep learning models for object detection on the i.MX8M Plus? Popular deep learning models for object detection on the i.MX8M Plus include YOLO (You Only Look Once), SSD (Single Shot Detector), and Faster R-CNN (Region-based Convolutional Neural Networks).
Q4: How does the VPU on the i.MX8M Plus accelerate object detection? The VPU on the i.MX8M Plus accelerates object detection by offloading compute-intensive tasks like convolutional neural network (CNN) inference, freeing up the CPU for other tasks.
Q5: What are some applications of object detection on the i.MX8M Plus Smart Board? Applications of object detection on the i.MX8M Plus Smart Board include robotics, surveillance, smart home devices, and industrial automation.
Q6: Can I use pre-trained models for object detection on the i.MX8M Plus? Yes, you can use pre-trained models for object detection on the i.MX8M Plus. Many deep learning frameworks like TensorFlow and PyTorch provide pre-trained models that can be fine-tuned for specific applications.
Q7: How do I optimize my object detection model for the i.MX8M Plus? You can optimize your object detection model for the i.MX8M Plus by using techniques like quantization, pruning, and knowledge distillation to reduce the model's size and increase its inference speed.
Q8: Can I use the i.MX8M Plus Smart Board for real-time object detection? Yes, the i.MX8M Plus Smart Board can be used for real-time object detection. The board's VPU and CPU work together to accelerate inference and provide fast and accurate results.
Q9: What are some tools and frameworks available for object detection on the i.MX8M Plus? Tools and frameworks available for object detection on the i.MX8M Plus include OpenCV, TensorFlow, PyTorch, and Caffe.
Q10: Can I use the i.MX8M Plus Smart Board for edge AI applications? Yes, the i.MX8M Plus Smart Board is well-suited for edge AI applications that require real-time processing and low latency, such as smart home devices and industrial automation.


Rank Pioneers/Companies Description
1 NVIDIA Developed the NVIDIA Jetson Nano module, which is compatible with the i.MX8M Plus Smart Board, enabling object detection capabilities.
2 Google Released the TensorFlow Lite framework, which supports object detection on the i.MX8M Plus Smart Board using the Arm NN SDK.
3 NXP Semiconductors Developed the eIQ Machine Learning Software Development Kit, which enables object detection on the i.MX8M Plus Smart Board using TensorFlow and Caffe.
4 Arm Released the Arm NN SDK, which provides a software framework for accelerating machine learning workloads, including object detection, on the i.MX8M Plus Smart Board.
5 STMicroelectronics Developed the STM32Cube.AI, a software framework that enables object detection on the i.MX8M Plus Smart Board using TensorFlow Lite and Arm NN SDK.
6 Intel Released the OpenVINO toolkit, which supports object detection on the i.MX8M Plus Smart Board using Intel's distribution of OpenCV.
7 Qualcomm Developed the Qualcomm Neural Processing Engine (NPE), which enables object detection on the i.MX8M Plus Smart Board using TensorFlow and Caffe.
8 Texas Instruments Released the TI Deep Learning framework, which supports object detection on the i.MX8M Plus Smart Board using TensorFlow and Arm NN SDK.
9 Cisco Systems Developed the Cisco Object Detection solution, which uses the i.MX8M Plus Smart Board to enable object detection capabilities in various industries.
10 Microsoft Released the Azure Machine Learning framework, which supports object detection on the i.MX8M Plus Smart Board using TensorFlow and Arm NN SDK.


Component Technical Details
Hardware Platform i.MX 8M Plus Smart Board, featuring NXP i.MX 8M Plus processor with:
  • Cortex-A53 CPU core @ 1.5 GHz
  • Vivante GC7000UL GPU
  • 2x MIPI-CSI camera interfaces
  • Wi-Fi and Bluetooth connectivity
Operating System Linux-based:
  • Kernel version: 4.14.98 or later
  • File system: ext4 or similar
  • Device tree overlay support
Object Detection Algorithm TensorFlow Lite-based:
  • Model architecture: MobileNet-SSD or similar
  • Input resolution: 300x300 pixels (RGB)
  • Output format: bounding box coordinates and class labels
Image Processing Pipeline Using OpenCV library:
  • Image capture from MIPI-CSI camera interface
  • Image resizing and normalization
  • Object detection using TensorFlow Lite model
  • Bounding box drawing and class label overlay
Performance Optimization Using various techniques:
  • Neon instruction set optimization for image processing
  • GPU acceleration using Vivante GC7000UL GPU
  • TensorFlow Lite model pruning and quantization
  • Multi-threading and parallel processing
Power Consumption Average power consumption:
  • Idle mode: ~100mW
  • Active mode (object detection): ~500-700mW