DSE-YOLO: Detail semantics enhancement YOLO for multi-stage strawberry detection

Multi-stage strawberry fruits detection is one of the important clues to estimate crop yields and assist robotic picking in modern agricultural production. However, it is difficult for detecting strawberries due to their small size, foreground-foreground class imbalance, and complex natural environment. Many works focus on how to detect fruits while ignoring multi-stage fruit detecting problems. In this paper, we propose DSE-YOLO (Detail-Semantics Enhancement You Only Look Once) to detect multi-stage strawberries. In DSE-YOLO, DSE (Detail-Semantics Enhancement) module is designed for detecting small fruits and distinguishing different stages of the fruit with higher accuracy, which utilize pointwise convolution and dilated convolution to extract various detail and semantics features in the horizontal and vertical dimensions. Exponentially Enhanced Binary Cross Entropy (EBCE) and Double Enhanced Mean Square Error (DEMSE) loss function are constructed to focus on small fruits, which can deal with foreground-foreground class imbalance problem. Experiments conducted on datasets demonstrate the superiority of DSE-YOLO over state-of-the-arts. The detection results had a mAP value of 86.58% and an F-1-Score value of 81.59%, which demonstrates the effectiveness of the proposed model. Especially, DSEYOLO can almost detect every stage of strawberry fruit accurately in the natural scene, which can provide an important theoretical basis and premise for automatic picking and monitoring system.

Automated summary

This paper proposes a method called DSE-YOLO for detecting multi-stage strawberries. The method utilizes a Detail-Semantics Enhancement module and specific loss functions to improve accuracy. Experimental results demonstrate the effectiveness of the proposed method and its potential for application in automatic picking and monitoring systems.