YOLOv5s

Embedded AI Series – Analysis of YOLOv5 Model Deployment Example Code on RK RV1126 Platform – 2 Memory Distribution of Model Outputs

by
Embedded AI Series - Analysis of YOLOv5 Model Deployment Example Code on RK RV1126 Platform - 2 Memory Distribution of Model Outputs

★ Embedded AI Series – Analysis of YOLOv5 Model Deployment Example Code on RK RV1126 Platform – 2 Memory Distribution of Model Outputs ” 1. Introduction The remaining important part of the source code in rknn_yolov5_demo is the post-processing. The post-processing of the official YOLOv5 model is included within the model, so no post-processing is … Read more

Improved YOLOv5s Algorithm for Solar Cell Defect Detection

by
Improved YOLOv5s Algorithm for Solar Cell Defect Detection

Introduction Solar energy, as a renewable energy source, has characteristics such as abundant reserves, permanence, cleanliness, pollution-free, and local sourcing, making it a consensus among countries to promote the development of new energy, especially in the photovoltaic industry. Therefore, quickly and accurately detecting solar cell defects has become an important issue for ensuring the production … Read more

Small Object Detection Using SIMD Dataset in YOLO Format

by
Small Object Detection Using SIMD Dataset in YOLO Format

Small object detection, SIMD dataset, YOLO format. The SIMD dataset has most images measured at 1024 × 768 pixels. SIMD (Haroon et al., 2020) is a target detection dataset proposed by the National University of Sciences and Technology in Pakistan, primarily for vehicle detection, containing 5000 remote sensing images (image size: 1024×768) and 45096 instances. … Read more

Embedded AI Series – Analysis of YOLOv5 Model Deployment Example Code on RK RV1126 Platform – Part 1

by
Embedded AI Series - Analysis of YOLOv5 Model Deployment Example Code on RK RV1126 Platform - Part 1

★ Embedded AI Series – Analysis of YOLOv5 Model Deployment Example Code on RK RV1126 Platform – Part 1 ” 1. Introduction In the previous article “Embedded AI Series – Deploying YOLOv5 Model on RK RV1126 Platform”, we successfully compiled and ran the official YOLOv5 model deployment example code from RK. However, this is just … Read more

Embedded AI Series – Deploying YOLOv5 Model on RK Platform RV1126

by
Embedded AI Series - Deploying YOLOv5 Model on RK Platform RV1126

★ Embedded AI Series – Deploying YOLOv5 Model on RK Platform RV1126 ” 1. Introduction The official RK example code includes a deployment example for YOLOv5, so we do not need to start from scratch as we did in previous articles for deploying YOLOv3. We can first run the official YOLOv5 example code from RK … Read more

PCB Defect Detection Based on Deep Learning YOLOv8 and YOLOv5

by
PCB Defect Detection Based on Deep Learning YOLOv8 and YOLOv5

Abstract Printed Circuit Boards (PCBs) play a crucial role in electronic devices, and defect detection during their manufacturing process is a key step in ensuring product quality. In recent years, deep learning technologies have made significant progress in object detection tasks, particularly with the YOLO (You Only Look Once) series of algorithms, which excel in … Read more

Setting Up the RK3588 NPU Development Environment

by
Setting Up the RK3588 NPU Development Environment

How to set up the RK3588 NPU development environment on an Ubuntu system (PC)? This involves running the Ubuntu system on the computer while the RK3588 board runs the Buildroot Linux system, establishing the RK3588 NPU development environment.The following are the corresponding deployment steps:0. Dependency Files RKNPU2 project download link: https://github.com/airockchip/rknn-toolkit2/tree/master/rknpu2 RKNN-Toolkit2 project download link: … Read more

Using the RK3588 Chip NPU: Running and Interpreting the Official rknn_yolov5_android_apk_demo

by
Using the RK3588 Chip NPU: Running and Interpreting the Official rknn_yolov5_android_apk_demo

1. Objective of This Article This article will accomplish two tasks: Run the official dynamic target recognition example using the camera on the RK3588 development board. Interpret the source code to enhance understanding of the rknn development. 2. Development Environment Description Host System: Windows 11 Target Device: Android development board equipped with RK3588 chip Core … Read more

Using the RK3588 Chip NPU: Running YOLOv5 Object Detection Model on Windows 11 with RKNN Docker

by
Using the RK3588 Chip NPU: Running YOLOv5 Object Detection Model on Windows 11 with RKNN Docker

Objective of This Article This article will detail how to configure the RKNN Docker environment on an Android development board equipped with the RK3588 chip in a Windows 11 system environment, and how to run the YOLOv5 object detection model accelerated by NPU on the development board using the adb tool. Development Environment Description Host … Read more

Practical Guide to Achieving 10x Inference Speed with YOLOv5: Model Deployment Using TensorRT on Jetson NX

by
Practical Guide to Achieving 10x Inference Speed with YOLOv5: Model Deployment Using TensorRT on Jetson NX

Follow our public account to discover the beauty of CV technology This article is adapted from the Frontier of Aerial Robotics, authored by Liang Jiachen, an engineer at Westlake University. With the continuous improvement of computing power and the growth of data, deep learning algorithms have made significant advancements. These algorithms are increasingly applied across … Read more