YOLO-iCBAM: an improved YOLOv4 based on CBAM for defect detection

Junqi Bao; Xiaochen Yuan

doi:10.1117/12.3023071

28 March 2024 YOLO-iCBAM: an improved YOLOv4 based on CBAM for defect detection

Junqi Bao, Xiaochen Yuan

Author Affiliations +

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130911P (2024) https://doi.org/10.1117/12.3023071
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

Defect detection in Photovoltaic (PV) cell Electroluminescence (EL) images is a challenge in industry. In this paper, a novel defect detection method YOLOv4 with an improved Convolutional Block Attention Module (YOLO-iCBAM) is proposed for PV cell EL images. We first propose an improved CBAM to enhance the network’s ability to capture multi-scale defects in complex image backgrounds. Then, we modify the conventional YOLOv4 architecture for defect detection. Specifically, we adjust the backbone network to make a fast convergence. Then, we adopt the iCBAM to YOLOv4 to refine the feature map before YOLO Head. Then, we train a K-Means++ model based on PV cell EL images to generate anchors for bounding box regression. Moreover, we conduct experiments in the PVEL-AD dataset to evaluate the proposed YOLO-iCBAM. The experimental results indicated that the proposed YOLO-iCBAM achieves a better F1-Score of 0.716 and mAP of 0.748.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junqi Bao and Xiaochen Yuan "YOLO-iCBAM: an improved YOLOv4 based on CBAM for defect detection", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130911P (28 March 2024); https://doi.org/10.1117/12.3023071

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