YOLO-HMC: An Improved Method for PCB Surface Defect Detection

Yuan, Minghao; Zhou, Yongbing; Ren, Xiaoyu; Zhi, Hui; Zhang, Jian; Chen, Haojie

doi:10.1109/tim.2024.3351241

Cited by 16 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the advancements in unmanned aerial vehicles (UAVs) and object detection technology, UAV object detection has emerged as an essential component in numerous domains, such as agriculture [1], surveillance [2,3], disaster response [4], infrastructure inspection [5], and many other fields [6][7][8][9]. The use of UAVs allows for efficient and flexible data collection over large or inaccessible areas, making them a valuable tool for tasks that benefit from aerial perspectives.…”

Section: Introductionmentioning

confidence: 99%

Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images

Yue,

Zhang,

Huang

et al. 2024

Drones

View full text Add to dashboard Cite

The task of multiple-tiny-object detection from diverse perspectives in unmanned aerial vehicles (UAVs) using onboard edge devices is a significant and complex challenge within computer vision. In order to address this challenge, we propose a lightweight and efficient tiny-object-detection algorithm named LE-YOLO, based on the YOLOv8n architecture. To improve the detection performance and optimize the model efficiency, we present the LHGNet backbone, a more extensive feature extraction network, integrating depth-wise separable convolution and channel shuffle modules. This integration facilitates a thorough exploration of the inherent features within the network at deeper layers, promoting the fusion of local detail information and channel characteristics. Furthermore, we introduce the LGS bottleneck and LGSCSP fusion module incorporated into the neck, aiming to decrease the computational complexity while preserving the detector’s accuracy. Additionally, we enhance the detection accuracy by modifying its structure and the size of the feature maps. These improvements significantly enhance the model’s capability to capture tiny objects. The proposed LE-YOLO detector is examined in ablation and comparative experiments on the VisDrone2019 dataset. In contrast to YOLOv8n, the proposed LE-YOLO model achieved a 30.0% reduction in the parameter count, accompanied by a 15.9% increase in the mAP(0.5). These comprehensive experiments indicate that our approach can significantly enhance the detection accuracy and optimize the model efficiency through the organic combination of our suggested enhancements.

show abstract

Section: Introductionmentioning

confidence: 99%

Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images

Yue,

Zhang,

Huang

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

Defect detection of printed circuit board surface based on an improved YOLOv8 with FasterNet backbone algorithms

Liu,

Zhang,

Karimi

2024

SIViP

View full text Add to dashboard Cite

An Improved YOLOv9 and Its Applications for Detecting Flexible Circuit Boards Connectors

Huang,

et al. 2024

Int J Comput Intell Syst

View full text Add to dashboard Cite

Flexible circuit boards are a cornerstone of the modern electronics industry. In automatic defect detection, FPC connectors present challenges such as minimal differences between oxidation defects and the background, easy degradation of Intersection over Union (IoU) scores, and significant variations in the shapes of black defect boundaries. Consequently, existing algorithms perform poorly in this task. We improve model YOLOv9 by introducing Multi-scale Dilated Attention (MSDA) on the output side to enhance the ability to capture features, and Deformable Large Kernel Attention (DLKA) on the other side of the output header to improve the ability to adapt to complex defect boundaries. Our use of IoU loss completely eliminates the risk of IoU degradation or gradient vanishing. Furthermore, we reduce computational overhead with the implementation of Faster Block. Following these improvements, the mean Average Precision (mAP) at 75% IoU (mAP75) for oxidized defects increased by 7.5% relative to the base model. Similarly, the mAP at 50% IoU (mAP50) for black defects increased by 5.7%, validating the relevance and efficacy of our proposed improvements. Overall, the average mAP50, mAP75, and mAP50:95 for all defects improved by 3.8%, 2.0%, and 2.3%, respectively. The performance gain achieved by our enhanced model significantly exceeds the improvement of YOLOv9 relative to YOLOv8.

show abstract

YOLO-HMC: An Improved Method for PCB Surface Defect Detection

Cited by 16 publications

References 40 publications

Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images

Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images

Defect detection of printed circuit board surface based on an improved YOLOv8 with FasterNet backbone algorithms

An Improved YOLOv9 and Its Applications for Detecting Flexible Circuit Boards Connectors

Contact Info

Product

Resources

About