Tea Buds Detection in Complex Background Based on Improved YOLOv7

Aiming at the problem that the color of tea buds is highly similar to the background in complex scenes and it is difficult to identify the buds, this study proposed an improved YOLOv7 algorithm by replacing the original convolution blocks with Depth Separable Convolution (DS Conv) blocks, and adding Convolutional Block Attention Modules (CBAM) and Coordinate Attention (CA) modules. The method improved mean Average Precision (mAP) by 1.28% and mean Recall (mR) rate by 2.92%, the final mAP and mR reached 96.70% and 93.88%, respectively, and 30.62 Frame Per Second (FPS) of the improved model meets the requirements of real-time detection. The results show that the detection accuracy of the improved YOLOv7 algorithm for tea buds was higher than that of other target detection algorithms, and the detecting performance is not significantly affected by the light conditions, and the recognition accuracy of tea buds at each growing period was excellent and balanced. This study provides experience for the realization of intelligent tea picking.

Automated summary

This study proposes an improved YOLOv7 algorithm by using Depth Separable Convolution (DS Conv) blocks, Convolutional Block Attention Modules (CBAM), and Coordinate Attention (CA) modules to solve the problem of difficult identification of tea buds due to their similar color with the background in complex scenes. The method improves the mean Average Precision (mAP) by 1.28% and mean Recall (mR) rate by 2.92%, achieving final mAP and mR of 96.70% and 93.88% respectively. The improved model also meets real-time detection requirements with a frame rate of 30.62 FPS. The results show that the improved YOLOv7 algorithm has higher detection accuracy for tea buds compared to other target detection algorithms, and it performs well under different light conditions, providing valuable insights for intelligent tea picking.