AI Object Detection Solution with YOLOv5 | Deep Learning | Machine Learning

 

In the past few years, the rapid development of AI deep learning technologies has dramatically accelerated the momentum of object detection. Object detection is at the core of most vision-based AI software and programs and plays a crucial role in scene understanding. Automatic analysis of images and videos to detect, identify, and count different items, animals, and people is commonly used in security, traffic, medical, and military applications.

Despite significant advances in the field and the power of computer vision, detecting objects is a complex implementation that often faces quite a few challenges. Object detection is a very complex task involving visual classification and object localization. Challenges in object detection include:

1. Objects may look completely
different from different viewpoints

2. The darker the light, the lower the visibility of objects

3. Messy backgrounds affect
recognition

4. Objects in motion require more precise algorithms

Popular algorithms used to perform object detection include R-CNN (Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO. The R-CNN models may generally be more accurate, yet the YOLO family of models is fast, much faster than R-CNN, achieving object detection in real-time.

Referring to this benchmark (YOLOv5 TensorRT Benchmark for NVIDIA® Jetson™ AGX Xavier™ and NVIDIA® Laptop 1),we also tested the very popular YOLOv5 object detection with the Blade 3 in hand to see how it works on the RK3588 chip and show its performance. 

Here, we show you detailed tutorial resources to do YOLO object detection for real-time camera feeds and videos.


Resource		 Link

Benchmark
 https://community.mixtile.com/t/blade-3-yolov5-benchmark/607
Real-time Camera Detection https://community.mixtile.com/t/how-to-change-the-rk-yolov5-demo-to-capture-real-time-camera-feeds/609
Object Detection for Videos https://community.mixtile.com/t/how-to-do-object-detection-in-video-with-rk-yolov5-demo/622

Additionally, we also made a detailed video for Blade 3 board to assess its performance and demonstrate its effectiveness. In this video, we’ll show you how to use our single board computers to do amazing RK YOLOv5 Object Detection.

Deep Learning Tech and AI Object Detection — Powered by Our SBCs

Mixtile Blade 3

As the previous benchmark mentioned, Mixtile Blade 3 board is very beneficial for any cost-effective AIOT and object detection solution for its core functionalities of the accuracy of NPU, lower power consumption and reasonable cost. 

Mixtile Edge 2

Compared with Blade 3 board, Mixtile Edge 2 has 1Top NPU, but it also performs very well. Features Quad-core ARM Cortex-A55 up to 2.0GHz and supports LPDDR4 up to 8GB. It adopts the SoC of RK3568 with integrated high-performance NPU, ideal for AI deep learning frameworks of TensorFlow, Caffe, Tflite, Pytorch, Onnx NN, Android NN, etc.

Powerful 6 TOPS NPU for Machine Learning

At the heart of Mixtile Blade 3 is the RK3588 SoC, an 8nm-fabricated, low-power and high-performance processor with quad-core Cortex-A76 and quad-core Cortex-A55. There is also a powerful, quad-core Mali G610MC4 GPU and a 6-TOPS NPU, enhancing AI workload performance and providing extensive machine learning support. NPU is specifically designed to perform complex mathematical calculations that are required for deep learning tech. This makes it highly efficient for processing large amounts of data, which is essential for yolo object detection tasks.

RK 3588 NPU

  • New multi-core self-developed architecture
  • Improve utilization of bandwidth
  • Pre-processing acceleration support
  • Improved performance of Eltwise
  • Added INT4/TF32 data type
  • INT8 computing power up to 6TOPS

Accurate Image Processing Access to AI Object Detection

The system-on-chip comes with a 48-megapixel image signal processor that can implement several algorithm accelerators such as HDR, 3A, 3DNR, sharpening, dehaze, fisheye correction, and gamma correction.

Advanced Video Streaming Processing for AI Deep Learning

Thanks in part to a built-in HDMI interface and onboard support for the encoding and decoding of high-quality video formats, it is equally well suited to a broad range of other applications—from personal computing to video streaming. The chip includes a powerful Arm Mali-G610 GPU, which can provide high-quality graphics processing and support for advanced graphics APIs, such as Vulkan and OpenGL ES 3.2. This is important for object detection applications that require advanced visualizations or user interfaces.

Advantages of Mixtile AI Object Detection Solution

Enables you to detect and classify objects in your images and videos in real-time to help you maximize your efficiency in AI deep learning.

1.Visual AI Deep Learning Acceleration

When it comes to object detection using AI and machine learning, efficiency and accuracy are crucial factors to consider. YOLO (You Only Look Once) is a deep learning algorithm that has gained popularity for its accurate object detection capabilities. Additionally, Mixtile Blade 3 is designed to consume low power, making it an eco-friendly option for object detection with high efficiency.

2. Empower Diverse AIOT Industrials:

Intelligent Healthcare Monitoring

Industrial Anomaly Detection

Autonomous Checkout

Express Sorting

Autonomous Vehicles

Precision Farming

3. Supports Wide Range of Connectivity Option

For successful object detection using AI and machine learning, the interface between the object detection system and peripheral devices is crucial. High-speed interfaces such as PCIe 3.0, USB 3.1, and HDMI 2.1. This means that it can easily interface with a wide range of sensors, cameras, and other peripherals that are commonly used in AI object detection applications.

4. Build Specific Businesses with Premium Solution

Our single board computers could be scaled to meet the needs of a wide range of applications, from small-scale projects to large-scale deployments. It could be easily integrated with your cloud-based solutions or other distributed systems for deep learning and machine learning projects.

Why do you choose Mixtile for AI/ML Projects?

Professional Support

Dedicated account manager; Over 10 years of R&D technical innovation and more than 15 people professional support team

Full Product Build Services

Mixtile’s premium R&D, production and after-sales service will transform your design concept into a saleable product

Complete Technical Resources

It offers application customization and integration services according to customer needs. Complete resources including SDK, development documents, technical documents and tutorials will be provided.

SDK

Development Documents

Technical Documents

Tutorials