Rocket Self-learning Control based on Lightweight Neural Network Architecture Search

Lu, Kunfeng; Yu, Chunmei; Yao, Na; Wang, Ludi; Zhao, Jikang

doi:10.1109/icus55513.2022.9986957

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Li et al [37] improved the mAP value to 98.71% by replacing the convolution layer in the trunk with the residual structure unit CSP based on the YOLOv4 algorithm. Wang et al [38] proposed a lightweight model that used the ShuffleNetV2 structure in the YOLOv5 backbone and achieved an accuracy of 95%. YOLOv7, as a classic representative of the target detection algorithm, has surpassed the previous YOLO series in detection speed and accuracy.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Detection Method of PCB Defect Based on Improved YOLOv7

Yang

Kang

2023

Electronics

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Printed circuit boards (PCBs) are a critical component of modern electronic equipment, performing a crucial role in the electronic information industry chain. However, accurate detection of PCB defects can be challenging. To address this problem, this paper proposes an enhanced detection method based on an improved YOLOv7 network. First, the SwinV2_TDD module is proposed, which adds a convolutional layer to extract the local features of the PCB. Then, the Magnification Factor Shuffle Attention (MFSA) mechanism is introduced, which adds a convolutional layer to each branch of the Shuffle Attention (SA) to expand its depth and enhance the adaptability of the attention mechanism. The SwinV2_TDD module and MFSA mechanism are integrated into the YOLOv7 network, replacing some ELAN modules and changing the activation function to Mish. The evaluation indexes used are Precision (P), Recall (R), and mean Average Precision (mAP). Experimental results show that the enhanced method achieves an AP of 98.74%, indicating a significant improvement in PCB defect detection performance.

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Section: Related Workmentioning

confidence: 99%