Xiaoyu Xu
ABSTRACT
Object detection is an important research branch in the field of computer vision, and personnel detection has rich application scenarios and use values. Deep learning is widely used in the field of personnel detection, but the traditional convolutional neural network is complex and needs the support of high computing power GPU, which is difficult to deploy on embedded devices. At the same time, the lack of feature information caused by too small scale or occlusion is the main reason for the reduction of personnel detection accuracy. To solve these problems, this paper proposes a lightweight personnel detection network based on reinforcement feature learning; At the same time, convolution can be separated by the depth of channel mixing mechanism to further reduce network parameters; Secondly, this paper designs a hole convolution module to obtain more discriminative feature information, and uses the hole space pyramid pool structure and the attention mechanism with position information to carry out effective feature fusion, thus improving accuracy and reasoning speed. Experiments on multiple data sets and multiple hardware platforms show that the proposed algorithm is better than the original YOLOv4 micro-network in terms of accuracy, speed, model parameters and volume, and is more suitable for deployment in embedded devices with limited resources.
- Girshick R, Donahue J, Darrell T, Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.Google Scholar
- Liu W, Anguelov D, Erhan D, Ssd: Single shot multibox detector[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer International Publishing, 2016: 21-37.Google Scholar
- 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. 2020: 10781-10790.Google Scholar
- Redmon J, Divvala S, Girshick R, You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.Google Scholar
- Liu W, Liao S, Hu W, Learning efficient single-stage personnel detectors by asymptotic localization fitting[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 618-634.Google Scholar
- Aaryn Cheung, Cai Jianyong, Li Ke, Cheng Yu, Zeng Yuanqiang. Detection Method of Indoor Personnel Based on DE-YOLO [J]. Computer System Application, 2020,29 (01): 203-208. DOI: 10.15888/j.cnki.csa.007240Google ScholarCross Ref
- Han Yue Research on Small Target Detection Algorithm Based on Multiscale Feature Fusion [D]. Northern Polytechnical University, 2021. DOI: 10.26926/d.cnki. gbfgu.2021.000096Google ScholarCross Ref
- Su Yang, Lu Xiang, Li Kun, Zhang Shaochao. Research on Computer Room Person Detection Based on Lightweight Deep Learning Networks [J]. Industrial Instrumentation and Automation Equipment, 2021, No.277 (01): 100-103Google Scholar
- 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. 2021: 13029-13038.Google Scholar
- Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020.Google Scholar
- Han K, Wang Y, Tian Q, Ghostnet: More features from cheap operations[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 1580-1589.Google Scholar
- Zhang X, Zhou X, Lin M, Shufflenet: An extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6848-6856.Google Scholar
- Yu F, Koltun V, Funkhouser T. Dilated residual networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 472-480.Google Scholar
- Chen L C, Papandreou G, Kokkinos I, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(4): 834-848.Google Scholar
- Sandler M, Howard A, Zhu M, Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520.Google Scholar
- He K, Zhang X, Ren S, Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904-1916.Google ScholarDigital Library
- Ghiasi G, Lin T Y, Le Q V. Dropblock: A regularization method for convolutional networks[J]. Advances in neural information processing systems, 2018, 31.Google Scholar
- Everingham M, Van Gool L, Williams C K I, The pascal visual object classes (voc) challenge[J]. International journal of computer vision, 2009, 88: 303-308.Google Scholar
- Chiu Y C, Tsai C Y, Ruan M D, Mobilenet-SSDv2: An improved object detection model for embedded systems[C]//2020 International conference on system science and engineering (ICSSE). IEEE, 2020: 1-5.Google Scholar
- Liu J, Liu L. Helmet wearing detection based on YOLOv4-MT[C]//2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE). IEEE, 2021: 1-5.Google Scholar
- Fang L, Wu Y, Li Y, Ginger seeding detection and shoot orientation discrimination using an improved YOLOv4-LITE network[J]. Agronomy, 2021, 11(11): 2328.Google ScholarCross Ref
- Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Ieee, 2005, 1: 886-893.Google ScholarDigital Library
- Ouyang W, Wang X. A discriminative deep model for personnel detection with occlusion handling[C]//2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012: 3258-3265.Google Scholar
- Khan R, Raisa T F, Debnath R. An efficient contour based fine-grained algorithm for multi category object detection[J]. Journal of Image and Graphics, 2018, 6(2): 127-136.Google ScholarCross Ref
- Hasegawa R, Iwamoto Y, Chen Y W. Robust Japanese road sign detection and recognition in complex scenes using convolutional neural networks[J]. Journal of Image and Graphics, 2020, 8(3): 59-66.Google ScholarCross Ref
Index Terms
- Lightweight Personnel Detection Network Based on Reinforcement Feature Learning
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