计算机工程与应用2024，Vol.60Issue(2)：304-313,10.DOI:10.3778/j.issn.1002-8331.2211-0282
面向嵌入式除草机器人的玉米田间杂草识别方法
Weed Identification Method in Corn Fields Applied to Embedded Weeding Robots
摘要
Abstract
In order to ensure the accuracy and rapidity of the embedded weeding robot in the corn field,a real-time target detection algorithm based on GBC-Yolov5s is proposed.First,the combination of the 1×1 convolution and depth-separable convolution is used to replace the traditional convolution,which reduces the redundant features generated by the back-bone network without changing the size of the output feature map.Secondly,a bidirectional feature fusion network(S-BiFPN)network is designed to enhance the ability of feature extraction,which can make full use of different scale fea-tures to improve the speed of weed detection and combine the multi-channel structure with the self-attention mechanism to enhance the attention of small targets by compressing and reweighting the input features.Finally,MWeed data sets are built for different environments to test the proposed algorithm.The results show that compared with the Yolov5s and Faster RCNN model algorithms,the size of the GBC-Yolov5s model after lightweight is only 3.3 MB,the detection time of the input image(GPU)reaches 15.6 ms,and the average accuracy(mAP)reaches 96.3%,which can effectively improve the target detection speed and recognition accuracy,and provide a theoretical basis for the field of intelligent agricultural weeding.关键词
YOLOv5s/目标识别/模型压缩/特征融合Key words
YOLOv5s/target identification/model compression/feature fusion分类
信息技术与安全科学引用本文复制引用
何全令,杨静文,梁晋欣,傅雷扬,滕杰,李绍稳..面向嵌入式除草机器人的玉米田间杂草识别方法[J].计算机工程与应用,2024,60(2):304-313,10.基金项目
农业农村部农业国际合作项目(125A0607). （125A0607）
