计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230900021-6.doi: 10.11896/jsjkx.230900021
李源鑫, 郭忠峰, 杨钧麟
LI Yuanxin, GUO Zhongfeng, YANG Junlin
摘要： 为提高现有集装箱的锁孔识别检测效率,减少算法参数量以及减小模型大小,提出了一种基于轻量化YOLOv5s的集装箱锁孔识别算法。该算法将YOLOv5s的Backbone主干特征提取网络部分更换为轻量级神经网络模型MobileNetV3,并对neck部分的特征融合结构进行进一步的优化,减少了模型的参数量和计算量,并提高了检测速度。引入注意力机制SimAM层,提高了检测的准确率和效率。使用不同的改进方法对模型进行重构后,在自建的集装箱锁孔数据集上进行训练和测试,并与改进的YOLOv5s进行对比实验。结果表明,改进后的模型大小仅为2.4MB,每幅图像的平均检测时间仅为5.1ms,平均检测精度达97.3%;与原始目标检测模型相比,该模型的大小减小了82.8%,检测速度提高了39%,在确保高检测精度的前提下展现出了较强的算法实时性。
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