|
|
|
| Improved Lightweight Pedestrian Target Detection Algorithm |
| JIN Mei,REN Tingting,ZHANG Liguo,YAN Mengxiao,SHEN Minghao |
| School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract A lightweight pedestrian detection algorithm based on feature fusion is proposed to solve the problem of low detection accuracy caused by dense pedestrian targets, small target scales, and varying background illumination around the target. Firstly, build a new backbone feature extraction network (CSPDarknet53-S), and add a new feature extraction module (REM) to the original backbone network to enhance the networks ability to extract pedestrian features. Secondly, improve the feature fusion structure. After extracting high-low feature maps from the backbone network, add a feature fusion module (RM block) between the backbone network and the feature fusion network to increase the receptive field. And then introduce shallow feature information to retain more small target features to form a new feature fusion network (IFFM). Finally, the fused feature map is processed through YOLO Head to obtain the output results. The above steps are based on the basic framework of TinyYOLOv4. Experimental results show that the proposed algorithm achieves higher detection accuracy and better detection results on pedestrian data sets (PASCAL VOC2007 and VOC2012 person data).
|
|
Received: 03 April 2023
Published: 21 February 2024
|
|
|
|
|
|
| ［2］ |
LIENHART R, MAYDT J. An extended set of Haar-like features for rapid object detection ［C］ //IEEE International Conference on Image Processing. New York, USA, 2002.
|
|
ZHANG L G, JIANG Y X, TIAN G J. Research on Unmanned Aerial Vehicle to Ground Vehicle Target Detection Algorithm Based on Multiscale Fusion Method ［J］. Acta Metrologica Sinica, 2021, 42(11): 1436-1442.
|
| ［7］ |
KAZEMI F M, SAMADI S, POORREZA H R, et al. Vehicle recognition using Curvelet transform and SVM ［C］ //4th International Conference on Information Technology. Las Vegas NV, USA, 2007.
|
| ［11］ |
张立国, 蒋轶轩, 田广军. 基于多尺度融合方法的无人机对地车辆目标检测算法研究［J］. 计量学报, 2021, 42(11): 1436-1442.
|
| ［18］ |
WANG C Y, LIAO H M, WU Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN ［C］ //IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle, WA, 2020.
|
| ［3］ |
DALAL N, TRIGGS B. Histograms of oriented gradients for human detection ［C］ //IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2005.
|
| ［5］ |
程淑红, 高许, 周斌. 基于多特征提取和SVM参数优化的车型识别［J］. 计量学报, 2018, 39(3): 348-352.
|
| ［6］ |
VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features ［C］ //IEEE Conference on Computer Vision and Pattern Recognition. Kauai, HI, USA, 2001.
|
| ［9］ |
GIRSHICK R. Fast R-CNN ［C］ //IEEE International Conference on Computer Vision. Santiago, Chile, 2015.
|
| ［15］ |
BOCHKOVSKIY A, WANG C Y, LIAO H M. Yolov4: Optimal speed and accuracy of object detection ［J/OL］. https://arxiv.org/abs/2004.10934. 2004.
|
| ［20］ |
ZHENG Z, WANG P, REN D, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation［J］. arXiv preprint arXiv: 2005. 03572, 2020.
|
| ［8］ |
GIRSHICK R, DONAHUE J, DARRELLl T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation ［C］ //IEEE Conference on Computer Vision and Pattern Rec-ognition. New York, USA, 2014.
|
| ［13］ |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, reatime object detection ［C］ //IEEE Conference on Computer Vision and Pattern Recognition. Seattle, WA, 2016.
|
| ［16］ |
HE K, ZHANG X, REN S, 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.
|
| ［19］ |
LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyramid networks for object detection ［C］ //IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, USA, 2017.
|
| ［21］ |
谭芳, 穆平安,马忠雪. 基于YOLOv3检测和特征点匹配的多目标跟踪算法［J］. 计量学报, 2021, 42(2): 157-162.
|
|
TAN F, MU P A, MA Z X. Multi-target Tracking Algorithm Based on YOLOv3 Detection and Feature Point Matching［J］. Acta Metrologica Sinica, 2021, 42(2): 157-162.
|
| ［22］ |
齐向明, 董旭. 改进Yolov7-tiny的钢材表面缺陷检测算法［J］. 计算机工程与应用, 2023, 59(12): 176-183.
|
|
QI X M, DONG X. Improved Yolov7-tiny algorithm for steel surface defect detection［J］. Computer Engineering and Applications, 2023, 59(12): 176-183.
|
| ［23］ |
WANG C Y, BOCHKOVSKIY A, LIAO H M. Scaled-YOLOv4: Scaling Cross Stage Partial Network ［C］ //IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Virtual, 2021.
|
|
WANG H B, YU F, LI Y J, et al. Detection of moving object by combining block features matching and local differential ［J］. Acta Metrologica Sinica, 2015, 36(4): 352-355.
|
| ［4］ |
WU B, NEVATIA R. Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors ［C］ //IEEE International Conference on Computer Vision. Beijing, China, 2005.
|
|
CHEN S H, GAO X, ZHOU B. Vehicle recognition based on multi-feature extraction and SVM parameter optimization ［J］. Acta Metrologica Sinica, 2018, 39(3): 348-352.
|
| ［10］ |
REN S, HE K, 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.
|
| ［17］ |
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module ［C］ //15th European Conference on Computer Vision. Munich, German, 2018.
|
| ［1］ |
王洪斌, 于菲, 李一骏, 等. 分块特征匹配与局部差分结合的运动目标检测［J］. 计量学报, 2015, 36(4): 352-355.
|
| ［12］ |
LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector ［C］ //14th European Conference on Computer Vision. Amsterdam, Netherlands, 2016.
|
| ［14］ |
REDMON J, FARHADI A. Yolov3: An incremental improvement ［J/OL］. https://arxiv.org/abs/1804.02767. 2018.
|
|
|
|
2024, Vol. 45 
