Automatic Urinary Sediments Visible Component Detection Based on Improved YOLO Algorithm

In this paper, the end-to-end object detection algorithm based on deep learning is used to analyze the urinary sediment visible component. In order to further improve the detection accuracy of the YOLOv3 algorithm on urine sediment visible component dataset, this paper presented a new way of determined the training sample, which can enhance the quality of training samples. Then we change the convolution kernel receptive field size of the feature fusion layer to increase the detection precision. The experimental results on the dataset show that the improved YOLOv3 has higher detection accuracy, which 5 categories of urinary sediment visible component. The results shows that our method obtain the best mean average precision (mAP) of 90.1%, which is better than the original YOLOv3 model 0.6% higher. The average detection time of the model is 0.047s per frame at 800 × 600 resolution.