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

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

Blog Article

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: echofix spring reverb 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 MAP@0.5 of 92.8%, reducing grandpas best 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.