山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (3): 115-121.doi: 10.6040/j.issn.1672-3961.0.2023.092
陈晓燕1,王川2,齐明杰1,张宁2,林晓龙1,霍延强3*,刘世杰4,田源3
CHEN Xiaoyan1, WANG Chuan2, QI Mingjie1, ZHANG Ning2, LIN Xiaolong1, HUO Yanqiang3*, LIU Shijie4, TIAN Yuan3
摘要： 针对传统灌溉渠异物入侵监测方法检测精度低、时效性差、夜间巡检不全面、危险性高等问题,提出一种基于雷视融合的灌溉区异物入侵监测方法。针对灌溉区周边行人、动物等小目标误检及特征提取能力不足等问题,提出一种基于YOLOv5改进的小目标识别算法,提高对灌溉区周边小目标检测能力。通过实际场景测试试验,本研究提出的灌溉区雷视融合监测方法和改进的基于YOLOv5的小目标识别算法,识别精确度达到93.26%,监测范围是设备周围360°,有效提升了不同时间段下的异物入侵监测能力,验证了该方法的准确性。
中图分类号:
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