Design of wheat ear detection in natural environment based on improved YOLOv5s

Aiming at the problems of slow target detection speed and low accuracy caused by occlusion, illumination and complex background environment interference in wheat ear detection of natural environment, the paper proposes to improve the network model based on YOLOv5s algorithm model to achieve fast and efficient target detection. The first improvement point is to add the attention mechanism. In the backbone and head parts, the CBAM attention mechanism module is added to reduce the interference of non-critical information, extract the key feature information of the image, and improve the detection accuracy. The second improvement point is to combine the lightweight network MobileNetV3 for target detection, using channel separable convolution and SE channel attention mechanism to improve the accuracy of target detection. The Shangmai 5226 as the research object, the optimized wheat ear database was built, and the training images were labeled and converted into yolo format. Under the operating environment of Pytorch, the improved YOLOv5s model was used to extract the image features of Shangmai 5226 series wheat ears with iteration of 300 times. The result was shown that compared with the single structure YOLOv5s model, the accuracy of the improved comprehensive network model is improved by 0.7 %, mAP reaches 91.1 %, which is increased by 1.3 %, and Fps reaches 25.821, which is increased by 5.7 %. The improved network model can effectively improve the speed and accuracy of wheat ear detection, improve the effect of wheat ear detection, and provide technical support for the development of agricultural automation and intelligent agriculture.