YOLOv5 with Optimized Clustering and CBAM for Controlled Knife Detection

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YOLOv5 with Optimized Clustering and CBAM for Controlled Knife Detection

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[1]ZHANG Zhen,CHEN Kexin,CHEN Yunfei.YOLOv5 with Optimized Clustering and CBAM for Controlled Knife Detection[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):40-45.[doi:10.13705/j.issn.1671-6833.2022.05.015]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 05 Page number: 40-45 Column: Public date: 2023-08-20

Title:: YOLOv5 with Optimized Clustering and CBAM for Controlled Knife Detection

Author(s):: ZHANG Zhen¹; CHEN Kexin²; CHEN Yunfei¹; 1. School of Information Engineering,Chang′an University, Xi′an 710018, China; 2. National Computer Network Emergency Response Technical Team / Coordination Center of China, Beijing 100029, China

Keywords:: controlled knife detection; public safety; target detection; clustering algorithm; attention mechanism

CLC:: :TP391

DOI:: 10.13705/j.issn.1671-6833.2022.05.015

Abstract:: Aiming at the problems that traditional controlled knife detection methods relied too much on specific equipment and environment, and the target detection was applicable to small scope of application, with weak anti-interference ability and low precision, a YOLOv5 model with optimized clustering and CBAM was proposed for controlled knife detection was proposed. Firstly, a decision diagram could be drew and cluster centers could be obtained by calculating the local density of each point and the minimum distance between it and others with higher density. Then the core points could be extracted by calculating all points in the local class the average distance with its class center, the global search assignment strategy was used to classify the test points. Finally, the statisticallearning strategy was employed to allocate the remaining points, these unprocessed points were used as noise points, and they were classified into the class of its nearest neighbor. The improved density peak clustering algorithm was used to analyze the bounding box of the controlled knife, optimize the size of the priori box, and improve the matching degree between the priori box and the size of target object, so as to solve the problem that the clustering effect of the K-means clustering algorithm in the YOLOv5 model such as its unstability and the low convergence rate of large-scale data. Moreover,The C3 module in the Backbone network was combined with the CBAM attention mechanism, named CBAMC3 module, which could improve the model′s ability to extract target features, solve the problem that the YOLOv5 algorithm was not effective for small target detection, and improve the model accuracy. The experimental results showed that the values of P, R, mAP @ 0. 5, mAP @ 0. 5 ∶ 0. 95 of the improved model YOLOv5-Plus on the customed data set were 98. 14%, 95. 80%, 97. 56%, and 76. 68%, respectived, which is 1. 64%, 1. 59%, 1. 51%, and 3. 26% higher than that of YOLOv5 before improvement, and also verified that the proposed model could effectively improve the detection performance of controlled knife in public areas.

References:: [1] 徐晨玉. 基于 YOLOv3 的危险物品检测模型研究与改进[D] . 兰州: 兰州大学, 2019.
XU C Y. Research and improvement of a dangerous goods detection model based on YOLOv3 [ D ] . Lanzhou: Lanzhou University, 2019.
[2] 杨希. 基于深度学习的地铁安检危险物品检测[ D] . 武汉: 武汉理工大学, 2020.
YANG X. Detection of dangerous goods in subway security check based on deep learning[D] . Wuhan: Wuhan University of Technology, 2020.
[3] 王胜. 毫米波图像中危险物品检测[ D] . 北京: 清华大学, 2018.
WANG S. Dangerous objects detection in millimeter wave images[D]. Beijing: Tsinghua University, 2018.
[4] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[5] HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN [C]∥2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2980-2988.
[6] 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.
[7] FERNANDEZ-CARROBLES M, DENIZ O, MAROTO F. Gun and knife detection based on faster R-CNN for video surveillance[C]∥Iberian Conference on Pattern Recognition and Image Analysis. Cham: Springer International Publishing,2019: 441-452.
[8] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection [EB / OL] . (2020-08-23) [2021-09-13] . https:∥ doi. org / 10. 48550 / arXiv. 2004. 10934.
[9] Ultralytics. YOLOv5[ EB / OL] . ( 2020- 05- 18) [ 2021- 08-12] . https:∥ github. com / ultralytics/ yolov5.
[10] LIU W, ANGUELOV D, ERHAN D, et al. Ssd: single shot multibox detector [ C ] ∥ European Conference on Computer Vision. Berlin:Springer Press, 2016: 21-37.
[11] 张伟彬, 吴军, 易见兵. 基于 RFB 网络的特征融合管制物品检测算法研究[ J] . 广西师范大学学报( 自然科学版) , 2021, 39(4) : 34-46. ZHANG W B, WU J, YI J B. Research on feature fusion controlled items detection algorithm based on RFB network[ J] . Journal of Guangxi Normal University ( Natural Science Edition) , 2021, 39(4) : 34-46.
[12] YUN S, HAN D, CHUN S, et al. CutMix: regularization strategy to train strong classifiers with localizable features [C]∥2019 IEEE / CVF International Conference on Computer Vision ( ICCV) . Piscataway: IEEE, 2019: 6022 -6031.
[13] 张震, 李浩方, 李孟洲, 等. 改进 YOLOv3 算法与人体信息数据融合的视频监控检测方法[ J] . 郑州大学学报(工学版) , 2021, 42(1) : 28-34.
ZHANG Z, LI H F, LI M Z, et al. Video surveillance detection method based on improved YOLOv3 algorithm and human body information data fusion [ J] . Journal of Zhengzhou University ( Engineering Science) , 2021, 42 (1) : 28-34.
[14] WANG C Y, MARK LIAO H Y, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[ C]∥2020 IEEE / CVF Conference on Computer Vision and Pattern Recognition Workshops ( CVPRW ) . Piscataway: IEEE, 2020: 1571-1580.
[15] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks [ J] . Science, 2014, 344 ( 6191) : 1492-1496.
[16] 薛小娜, 高淑萍, 彭弘铭, 等. 结合 k 近邻的改进密度峰值聚类算法[ J] . 计算机工程与应用, 2018, 54 (7) : 36-43.
XUE X N, GAO S P, PENG H M, et al. Improved density peaks clustering algorithm combining k-nearest neighbors[ J] . Computer Engineering and Applications, 2018, 54(7) : 36-43.
[17] WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]∥Proceedings of the European Conference on Computer Vision (ECCV) . Berlin:Springer Press,2018: 3-19.

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Last Update: 2023-09-04