Aiming at the problems of small separation range and low separation rate in the intelligent separation of coal and gangue, a threshold separation method based on dual-energy X-ray technology is proposed. Low-energy original image, use ratio method to obtain R-value image, integrate R-value image and low-energy image information to fit the R-L curve of coal and gangue, count characteristic values and draw sample distribution maps, calculate the sorting threshold of coal and gangue, and realize coal and the sorting of gangue. The experimental results show that the threshold sorting algorithm is more effective in improving large-size materials than the traditional R-value recognition algorithm and R-L curve fitting algorithm, and the recognition rate of 150-300mm materials is as high as 96.99% and 92.32%.
Common object detection algorithms have too many backgrounds, duplicated boxes, and a high miss ratio in detecting rotated objects, which limit their applications on the industrial site. To address these problems, this paper proposed an improved YOLOv5 to detect rotated objects. First, this paper used the K-means clustering algorithm to develop clustering analysis for trained datasets to confirm more proper anchor boxes to reduce the training time. Then this paper transferred the angle issue to a classification problem. This paper also learned angles in the original loss function combined with the circular smooth label (CSL) algorithm, thus avoiding the periodicity of angle regression. Last, this paper selected one from different detection results of an object to increase the accuracy of the detection results. The experiment showed that the proposed algorithm had a higher detection precision than other methods in the public dataset DOTA. When the proposed algorithm detected rotated objects in the dataset collected on the industrial site, its mAP reached 94.35%. This value was 8.20% higher than that of YOLOv5, satisfying the detection requirement on the industrial site.
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