ZHANG Simin, LIU Xinmei, YIN Junling, LI Baoling. PCB Defect Detection Method Based on Improved YOLOv7[J]. Computer and Modernization, 2024, 0(12): 45-52.
[1] MARQUES A C, CABRERA J M, DE FRAGA MALFATTI C. Printed circuit boards: A review on the perspective of sustainability[J]. Journal of Environmental Management, 2013,131:298-306.
[2] 李任鹏,李云峰. CNN融合多尺度特征的PCB裸板缺陷识别[J]. 智能计算机与应用, 2023,13(10):65-72.
[3] 邓璘. 基于机器视觉的PCB表面装配缺陷检测方法研究[D]. 武汉:武汉理工大学, 2019.
[4] YANG Y J, KANG H Y. An enhanced detection method of PCB defect based on improved YOLOv7[J]. Electronics, 2023,12(9). DOI: 10.3390/electronics12092120.
[5] 黄忠天,陈伟,李昭慧,等. 一种应用于PCB缺陷检测的改进SIFT算法[J]. 无线电工程,2023,53(6):1479-1486.
[6] 胡江宇,贾树林,马双宝. 基于改进级联Faster RCNN的PCB表面缺陷检测算法[J]. 仪表技术与传感器, 2022,(7):106-110.
[7] 刘涛,张涛. 基于GhostNet-YOLOv4算法的印刷电路板缺陷检测[J]. 电子测量技术, 2022,45(16):61-70.
[8] 季堂煜,赵倩,余文涛,等. 基于增强小目标特征提取的PCB板缺陷检测模型[J]. 仪表技术与传感器, 2023,(4):87-92.
[9] 卞佰成,陈田,吴入军,等. 基于改进YOLOv3的印刷电路板缺陷检测算法[J]. 浙江大学学报(工学版),2023,57(4):735-743.
[10] YI W G, WANG B. Research on underwater small target detection algorithm based on improved YOLOv7[J]. IEEE Access, 2023,11:66818-66827.
[11] LIU S G, WANG Y J, YU Q G, et al. CEAM-YOLOv7: Improved YOLOv7 based on channel expansion and attention mechanism for driver distraction behavior detection[J]. IEEE Access, 2022,10:129116-129124.
[12] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. Scaled-YOLOV4: Scaling cross stage partial network[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2021:13029-13038.
[13] IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]// International Conference on Machine Learning. Pmlr, 2015:448-456.
[14] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023:7464-7475.
[15] HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(9):1904-1916.
[16] DING X H, ZHANG X Y, MA N N, et al. Repvgg: Making vgg-style convnets great again[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2021:13733-13742.
[17] SANDLER M, HOWARD A, ZHU M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2018:4510-4520.
[18] CHEN J R, KAO S H, HE H, et al. Run, don't walk: Chasing higher FLOPS for faster neural networks[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2023:12021-12031.
[19] AWAIS M, IQBAL M T B, BAE S H. Revisiting internal covariate shift for batch normalization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020,32(11):5082-5092.
[20] ZHANG F H, ZHANG W, CHENG C S, et al. Detection of small objects in side-scan sonar images using an enhanced YOLOv7-based approach[J]. Journal of Marine Science and Engineering, 2023,11(11). DOI: 10.3390/jmse11112155
[21] TAN M, PANG R, LE Q V. Efficientdet: Scalable and efficient object detection[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2020:10781-10790.
[22] LI X, NIU W, YAN Y, et al. Detection of broken hongshan buckwheat seeds based on improved YOLOv5s model[J]. Agronomy, 2023, 14(1). DOI: 10.3390/agronomy140
10037.
[23] ZHENG Z H, WANG P, LIU W, et al. Distance-IoU loss: Faster and better learning for bounding box regression[C]// Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, 2020,34(7): 12993-13000.
[24] LI Z J, WEI H P. Research on parking detection algorithm based on yolov8[C]// Sixth International Conference on Computer Information Science and Application Technology. SPIE, 2023,12800:257-260.
[25] XIN Z M, LU T W, LI X H. Detection of train bottom parts based on XIoU[C]// Proceedings of the 2019 International Conference on Robotics Systems and Vehicle Technology. ACM, 2019:91-96.
[26] XING J J, JIA M P. A convolutional neural network-based method for workpiece surface defect detection[J]. Measurement, 2021,176. DOI: 10.1016/j.measurement.2021.109185.
[27] LI Z, YAN J, ZHOU J, et al. An efficient SMD-PCBA detection based on YOLOv7 network model[J]. Engineering Applications of Artificial Intelligence, 2023, 124. DOI: 10.1016/j.engappai.2023.106492.