Forestry Pests Detection and Identification Based on YOLOv5

The construction of forestry ecological environment monitoring is an urgent need for the healthy and sustainable development of forestry ecology. It is also the key to the protection of forest resources, the construction of ecological civilization and the improvement of forestry pest control system. Rapid, accurate and effective identification of forest pests can curb the spread of pests and diseases, promote the comprehensive management of forest pests and diseases, and reduce the harm to forestry production and ecological environment construction. In this paper, a deep learning method is proposed. Using the current powerful object detection algorithm YOLOv5 to achieve the detection and identification of forest pests. Overlapping and occluded objects often appear in pest images, so DIoU_NMS algorithm is used to select the target box to enhance the detection and identification accuracy of sheltered pests. Experimental results show that the proposed model can effectively identify nine forest pests in the dataset, with a precision of 0. 973, recall of 0. 929 and mean Average Precision (mAP) of 0. 942. Compared with YOLOv3 and Faster-RCNN, mAP is 0. 04 higher than YOLOv3 and 0. 087 higher than Faster-RCNN. It shows that the model has high recognition accuracy, good real-time performance and strong robustness.