Study on Corn Crop Pest Detection Based On Improved YOLOv7
Faced with the challenges of small target volumes,diverse morphologies,and uneven distributions of pests in maize crop pest detection,existing detectors suffer from issues such as false positives and false negatives.In response to above challenges,a maize crop pest detection algorithm SPD-YOLOv7 based on YOLOv7 is proposed.The maize pest dataset is created and collected,and the data augmentation technique is used to enlarge the data set.The SPD-Conv module is introduced to replace some step convolu-tion layers in the original backbone and head networks,and mitigate the loss of detailed information as the network deepens,thereby enhancing the model's ability to capture the features and positional information of small targets.By integrating the ELAN-W module with the CBAM attention mechanism,it enables the network to better learn pest features,suppress background noise,and focus on the target itself.The improved YOLOv7 network achieves an accuracy of 98.38％and mean average precision of 99.4％.Compared to the original YOLOv7 model,the accuracy and mean average precision improve by 2.46 and 3.19％,respectively.The enhanced al-gorithm is superior to mainstream algorithms such as Faster-RCNN,YOLOv3,YOLOv4,YOLOv5,and YOLOv6 in the detection ac-curacy,while maintaining real-time performance.Experimental results indicate that the proposed algorithm has the rapid identification of maize crop pest distributions and can be applied for real-time pest monitoring in practical agricultural fields.
万方数据
维普
