Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

He, Gang; Zhang, Jianyun; Hu, Yang; Yuan, Naichang; Zou, Huatao

doi:10.3390/electronics11193189

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Some researchers have proposed new structures to enhance the feature extraction capabilities of the network by reducing the loss of detailed information. For example, He G et al [ 29 ] introduced the SPP-D module, which reduces the feature information loss caused by max-pooling in the SPP module by enhancing the reuse of feature information, thereby improving small object detection capabilities. Sunkara R et al [ 30 ] utilized the SPD-Conv structure to replace the original downsampling structure, successfully reducing information loss and improving the algorithm’s capability to detect small objects.…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers have proposed their own improvement methods for this issue. For example, He G et al [ 29 ] used DIoU-NMS to replace the original NMS, successfully improving the detection accuracy of small objects in complex backgrounds. Zhang H et al [ 36 ] combined Cluster-NMS [ 37 ] with DIoU-NMS to propose the Cluster-DIoU-NMS method, which solves both over-suppression and overlapping issues.…”

Section: Related Workmentioning

confidence: 99%

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YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s

Sun

Chen

Xiao

et al. 2023

Sensors

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Due to the challenges of small detection targets, dense target distribution, and complex backgrounds in aerial images, existing object detection algorithms perform poorly in aerial image detection tasks. To address these issues, this paper proposes an improved algorithm called YOLOv5s-DSD based on YOLOv5s. Specifically, the SPDA-C3 structure is proposed and used to reduce information loss while focusing on useful features, effectively tackling the challenges of small detection targets and complex backgrounds. The novel decoupled head structure, Res-DHead, is introduced, along with an additional small object detection head, further improving the network’s performance in detecting small objects. The original NMS is replaced by Soft-NMS-CIOU to address the issue of neighboring box suppression caused by dense object distribution. Finally, extensive ablation experiments and comparative tests are conducted on the VisDrone2019 dataset, and the results demonstrate that YOLOv5s-DSD outperforms current state-of-the-art object detection models in aerial image detection tasks. The proposed improved algorithm achieves a significant improvement compared with the original algorithm, with an increase of 17.4% in mAP@0.5 and 16.4% in mAP@0.5:0.95, validating the superiority of the proposed improvements.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s

Sun

Chen

Xiao

et al. 2023

Sensors

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“…Moreover, the surface defects in camera modules are small objects, and the complex industrial contexts such as camera module fabrication demand high accuracy and real‐time target recognition. [ 13 ] Therefore, a target recognition algorithm that can detect small targets quickly and accurately is needed for camera module surface defect detection. However, the YOLO series target detection algorithm, despite its high detection efficiency, has difficulties in detecting small targets.…”

Section: Introductionmentioning

confidence: 99%

YOLOv7‐EAS: A Small Target Detection of Camera Module Surface Based on Improved YOLOv7

Zou

Yuan

et al. 2023

Advcd Theory and Sims

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The camera module surface may introduce defects in the focusing process, and manual detection of these defects is inefficient and costly. To address this problem, a method to automatically expand the dataset for deep learning models with fewer original images is developed. A defect detection method is also presented, YOLOv7‐EAS, that enhances YOLOv7 for camera module images with complex backgrounds and small target defects. This method introduces NewEIOU Loss, a bounding box regression loss function that extends EIOU Loss with a nonlinear compensation term. This function improves the model's convergence rate to quadratic order. Moreover, the detection head based on ASFF is redesigned to adapt the layer features and spatially filter out irrelevant information to improve the detection rate of small targets. Finally, a 3D attention mechanism is applied to increase the model's ability to extract fine‐grained features without increasing the network parameters. Experimental results show that the improved YOLOv7 algorithm achieves 90.5% mAP for the camera module image dataset, which is 10.7% higher than the original algorithm, at 136 FPS. This method can perform the camera module surface defect detection task more accurately and meet the real‐time requirements of the production line.

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