Mixed image detection method of belt coal blockage and leakage based on improved RetinaNet mode

The mixed image characteristics of coal blocking and coal leakage by belt conveying are complex, and the current identification method lacks timeliness. A hybrid image detection method based on improved RetinaNet model is proposed. This method uses CCD camera and lidar to construct a hybrid imaging system to realize three-dimensional imaging of coal conveying belt. An improved RetinaNet model is used to train the labeled three-dimensional image data set, and features are extracted and fused through attention mechanism and feature pyramid to detect coal blockage and coal leakage, and it is deployed in production environment to realize real-time monitoring. The test results show that this method can comprehensively collect the images of coal conveying by belt, and accurately identify the detection of coal blockage and coal leakage. This method is applied to the actual production environment, which can monitor the situation of belt coal transportation in real time and accurately detect coal blockage and leakage, which is of great significance to improve the production efficiency of coal transportation, reduce economic losses and ensure production safety.