A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion

• Published in: Entropy (Basel, Switzerland) Vol. 23; no. 12; p. 1587
• Main Authors: Zha, Mingfeng, Qian, Wenbin, Yi, Wenlong, Hua, Jing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.11.2021; MDPI
• Subjects: Accuracy; Agricultural production; Algorithms; attention mechanism; Automation; Convolution; Datasets; Deep learning; Entropy (Information theory); Feature extraction; feature fusion; Forestry; MobileNet; Network management systems; Neural networks; pest detection; Semantics; Sensors; YOLOv4; deep learning; MobileNet; feature fusion; YOLOv4; attention mechanism; pest detection
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e23121587

Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network's learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.