Defect detection for PV Modules based on the improved YOLOv5s

Xu, Shuwei; Qian, Huimin; Shen, Wei; Wang, Fangzheng; Liu, Xiquan; Xu, Zhangjin

doi:10.1109/cac57257.2022.10055183

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…To capture contextual information and improve detection performance by understanding pixel relationships, it is crucial to incorporate contextual information. Inspired by the average-pooling method, Xu et al [1] introduced a hybrid spatial pyramidal pooling module (H-SPP) [17]. Building upon H-SPP, we propose Cross-Channel-H-SPP (CH-SPP) and integrate it into the backbone layer.…”

Section: Cross-channel-h-sppmentioning

confidence: 99%

Capsule Defect Detection Method Based on an Improved Model

Zhao,

Dai,

Chen

et al. 2024

Advances in Transdisciplinary Engineering

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This article presents a novel method based on an enhanced version of the YOLOv5 model for detecting surface defects on capsules. The paper addresses the challenge of detecting defects on transparent capsules by introducing a deep learning-based approach called M-YOLO. Firstly, the backbone layer is replaced with MobileNetV3, enhancing the model’s suitability for scenarios with limited storage space and power consumption. Secondly, a Cross-channel-H-SPP (CH-SPP) module is devised to augment the contextual information within the sensory field. To enhance defect detection accuracy, the SE attention mechanism is incorporated. Additionally, an improved label assignment strategy is employed to enhance the recall rate. Experimental results on the dataset demonstrate significant improvements in both accuracy and speed compared to the YOLOv5 model. The algorithm proposed in this article satisfies the requirement of processing every second (specific data).

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Section: Cross-channel-h-sppmentioning

confidence: 99%