Laser Technology, Volume. 48, Issue 4, 534(2024)
An improved infrared object detection algorithm based on YOLOv5
[3] [3] BILAL M, HANIF M S. Benchmark revision for HOG-SVM pedestrian detector through reinvigorated training and evaluation methodologies[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 16(52): 1277-1287.
[4] [4] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hie-rarchies for accurate object detection and semantic segmentation[C]// Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE Press, 2014: 277-127.
[5] [5] LI Y, PANG Y, CAO J, et al. Improving single shot object detection with feature scale unmixing[J]. IEEE Transactions on Image Processing, 2021, 30: 2708-2721.
[6] [6] CHENG G, YUAN X, YAO X W, et al. Towards large-scale small object detection: Survey and benchmarks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 23(76): 34-46.
[7] [7] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]//Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE Press, 2016: 779-788.
[10] [10] JIANG P, DAJI E, LIU F, et al. A review of YOLO algorithm deve-lopments[J]. Procedia Computer Science, 2022, 199: 1066-1073.
[11] [11] TERVEN R, CORDOVA-ESPARAZA D M. A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond[J]. arXiv Computer Science, 2023, 4: 2304.00501.
[12] [12] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 75(23): 2004-10934.
[13] [13] ZHANG Y, GUO Zh Y, WU J Q, et al. Real-time vehicle detection based on improved YOLOv5[J]. Sustainability, 2022, 19: 12274-15427.
[14] [14] FANGBO Z, ZHAO H L, NIE Z. Safety helmet detection based on YOLOv5[J]. IEEE International Conference on Power Electronics, Computer Applications, 2021, 34(56): 6-11.
[15] [15] ZHU X K, LYU Sh Ch, WANG X, et al. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//International Conference on Computer Vision. Québec, Canada: IEEE Press, 2021: 11539.
[16] [16] HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C]//Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE Press, 2021: 13731-13722.
[17] [17] WOO S H, PARK J C, LEE J Y, et al. CBAM: Convolutional block attention module[C]//European Conference on Computer Vision. Munich, Germany: Springer Science Press, 2018: 3-9.
[18] [18] HU J, LI S, SUN G. Squeeze-and-excitation networks[C]//Confe-rence on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE Press, 2018: 7132-7141.
[19] [19] TAN M X, PANG R M, LE Q V. Efficientdet: Scalable and efficient object detection[C]//Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE Press, 2020: 10781-10790.
[20] [20] ZHANG Y F, REN W Q, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157.
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LIU Haojiao, LIU Lishuang, ZHANG Mingchun. An improved infrared object detection algorithm based on YOLOv5[J]. Laser Technology, 2024, 48(4): 534
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Received: Jul. 28, 2023
Accepted: Dec. 2, 2024
Published Online: Dec. 2, 2024
The Author Email: LIU Lishuang (Liulishaung@bistu.edu.cn)