Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model

In agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. However, traditional deep learning models face challenges in resource-constrained environments, such as insufficient accuracy, slow inference speed, and large model sizes, which hinder their practical application. To address these issues, this study proposes an improved YOLOv7-tiny model designed to deliver efficient, accurate, and lightweight pest detection solutions. The main improvements are as follows: 1. Lightweight Network Design: The backbone network is optimized by integrating GhostNet and Dynamic Region-Aware Convolution (DRConv) to enhance computational efficiency. 2. Feature Sharing Enhancement: The introduction of a Cross-layer Feature Sharing Network (CotNet Transformer) strengthens feature fusion and extraction capabilities. 3. Activation Function Optimization: The traditional ReLU activation function is replaced with the Gaussian Error Linear Unit (GELU) to improve nonlinear expression and classification performance. Experimental results demonstrate that the improved model surpasses YOLOv7-tiny in accuracy, inference speed, and model size, achieving a [email protected] of 92.8%, reducing inference time to 4.0 milliseconds, and minimizing model size to just 4.8 MB. Additionally, compared to algorithms like Faster R-CNN, SSD, and RetinaNet, the improved model delivers superior detection performance. In conclusion, the improved YOLOv7-tiny provides an efficient and practical solution for intelligent pest detection in agriculture and forestry.