Research on identification and detection of coal and gangue based on infrared thermal imaging technology and improved YOLOv8n

Considering that traditional image recognition methods for coal and gangue are susceptible to environmental factors such as light and dust, this paper proposes an image recognition and detection method for the coal and gangue based on infrared thermal imaging technology and an improved YOLOv8n. First, we established a thermal infrared imaging platform for coal and gangue. We used the YOLOv8n model to analyze the differences between coal and gangue at various temperatures, determining the optimal temperature for effective recognition of the coal and gangue. Next, to facilitate the practical deployment of the detection model for coal and gangue, we incorporated the Ghost module into the original YOLOv8n model to reduce its complexity and storage requirements. Unfortunately, incorporating the Ghost module led to a slight decrease in the model's detection performance. Next, we introduced the DAttention module into the model to enhance its feature extraction capabilities. At the same time, we replaced the CIoU loss function with DIoU, which further improved the model's performance metrics. Finally, we compared the improved YOLOv8n model with Faster-RCNN model and SSD model to validate its feasibility and accuracy further and provide a new approach for the detection of coal and gangue.