A small object detection algorithm for mine environment

The detection of protective equipment carried by underground mine operators is a crucial measure for preventing safety accidents and safeguarding personal life and property. However, current challenges include low object detection accuracy and difficulty detecting small objects, we propose a small object detection algorithm based on the improved You Only Look Once Version 8 (YOLOv8) for the mine environment. To minimize the semantic gap between features at different levels and enhance the feature fusion effect, the Asymptotic Feature Pyramid Network-Four (AFPN-F) has been designed to replace the Neck component of YOLOv8, enabling the detection model to better adapt to semantic information across varying levels. To enhance the model's sensitivity to small objects in the mine environment, a superficial feature output layer has been added to the model. This addition helps to prevent the loss of small-sized objects, which may contain limited feature information, during successive convolution operations. To address the significant differences in the scales of various objects in the mine, the More Focused Intersection over Union Loss (Focaler-IoU) is introduced as a loss function. This modification is intended to improve the handling of different types of regression samples, enhance training accuracy, and ensure that the model is more focused on small objects in the mine environment. The experimental results show that the proposed model outperforms other mainstream models. Compared to the baseline model YOLOv8, achieving an improvement of 3. 7 percent in mean Average Precision (mAP), the number of parameters has been reduced by 30 percent, resulting in a model size of only 5 Megabytes. This study provides an effective solution for detecting small objects in underground mines.

Authors

• Dong Liu University of Science and Technology of China
• Xin Zhao Ant Group, Shanghai, China
• Weiqiang Fan

Keywords

• Mine environmental
• Small object detection
• Deep learning
• Feature fusion
• Loss function
• You only look once