基于LID-YOLO的小目标昆虫轻量化检测算法  

Light Weight Detection Algorithm for Small Target Insects Based on Lightweight Insect Detection-YOLO

作　　者：陈中举[1] 李和平 许浩然 CHEN Zhongju;LI Heping;XU Haoran(School of Computer Science,Yangtze University,Jingzhou 434023,CHN)

机构地区：[1]长江大学计算机科学学院,湖北荆州434023

出　　处：《半导体光电》2024年第6期945-952,共8页Semiconductor Optoelectronics

基　　金：中国高校产学研创新基金项目(2023IT269)。

摘　　要：针对复杂背景下新疆棉田昆虫识别误检率高、小目标昆虫难以检测等问题,提出了一种基于YOLOv5s改进的轻量化昆虫检测模型(Lightweight Insect Detection-YOLO,LID-YOLO)。首先,主干网络使用GhostNet网络替换原CSPDarknet53网络,并采用Slim-Neck模块对颈部网络进行改进,以实现模型轻量化;其次,引入BottleNet Transformer融合模块,减少模型参数量并增强网络特征提取能力,更好检测小目标;最后,加入NAM注意力机制,通过应用权重稀疏性惩罚抑制不显著权重来提取细节特征,提高模型准确率。实验结果表明,LID-YOLO模型在参数量、计算量、模型权重大小方面,相比YOLOv5s模型,其分别减少了30.9%,45.6%和29.7%。LID-YOLO模型的准确率达到了97.4%,检测速度为55.25 FPS,与原YOLOv5s模型相比,提高了1个百分点和2.62 FPS。LID-YOLO模型在保证轻量化的同时进一步提高了检测精度,更好满足农作物昆虫实际检测的需要。Aiming at the problem of low insect identification accuracy and difficult detection of small target insects against the complex background of cotton fields in Xinjiang,a lightweight insect detection model based on YOLOv5s(LID-YOLO)is proposed.First,the GhostNet network is used to replace the original cross-stage partial CSPDarknet53 network in the backbone,and the Slim-Neck module is used to improve the neck network,to achieve a lightweight model.Second,the fusion module BottleNet Transformer is introduced to reduce the number of model parameters and enhance the capability of network feature extraction to better detect small targets.Finally,the normalization-based attention module(NAM)is added to extract detail features by applying a sparse weight penalty to suppress non-significant weights and improve model accuracy.The experimental results show that compared with YOLOv5s,the LID-YOLO model reduces the number of parameters,calculations,and model weight by 30.9%,45.6%,and 29.7%respectively.The accuracy rate of LID-YOLO model reached 97.4%,and detection speed was 55.25 FPS,which is 1%point and 2.62 FPS higher than that of the original YOLOv5s model.The LID-YOLO model not only ensures lightweight,but also improves detection accuracy to better meet the requirements of crop insect detection.

关 键 词：YOLOv5s BottleNet Transformer NAM 轻量化网络 目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]