A component recognition model based on improved attention mechanism and YOLOv3

Dailin Jiang; Yikai Wu; Pengcheng Xu; Jie Yang; Delan Zhang; Jiawei Liu; Zhengquan Lyu

doi:10.1117/12.3026313

27 March 2024 A component recognition model based on improved attention mechanism and YOLOv3

Dailin Jiang, Yikai Wu, Pengcheng Xu, Jie Yang, Delan Zhang, Jiawei Liu, Zhengquan Lyu

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310522 (2024) https://doi.org/10.1117/12.3026313
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In response to the insufficient feature information of smaller electronic components in the current YOLOv3 algorithm used in object detection, which leads to false detections, missed detections, and low average accuracy, this paper proposes an improved attention mechanism-based YOLOv3 algorithm for small object detection. Firstly, the model introduces a category weighting factor α to overcome the problem of imbalanced training samples, enhancing training robustness and improving classification accuracy. Secondly, an attention mechanism is introduced to learn the importance of each channel's characteristics, enabling weight allocation for primary and secondary information, suppressing secondary information, and eliminating some redundant information, effectively improving the learning direction of the model. Lastly, by incorporating the category weighting factor α and L2 regularization term into the loss function, the training of the network model can converge faster and more stably. The proposed algorithm achieves an average precision of 92.86% for resistor recognition, a 1.07% improvement compared to the classical YOLOv3 algorithm. For chip recognition, the average precision is 97.23%, an improvement of 0.8% compared to the classical YOLOv3 algorithm. These results demonstrate a certain enhancement in the detection performance of small objects.

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

Citation Download Citation

Dailin Jiang, Yikai Wu, Pengcheng Xu, Jie Yang, Delan Zhang, Jiawei Liu, and Zhengquan Lyu "A component recognition model based on improved attention mechanism and YOLOv3", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310522 (27 March 2024); https://doi.org/10.1117/12.3026313

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KEYWORDS

Detection and tracking algorithms

Feature extraction

Small targets

Target recognition

Image processing

Electronic components

Education and training

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