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首页> 外文会议>International Conference on Computer Vision, Image and Deep Learning >Automatic Urinary Sediments Visible Component Detection Based on Improved YOLO Algorithm
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Automatic Urinary Sediments Visible Component Detection Based on Improved YOLO Algorithm

机译：基于改进的Yolo算法的自动尿泥沉积物可见分量检测

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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.
机译：本文采用基于深度学习的端到端物体检测算法来分析尿泥沉积物可见部件。为了进一步提高尿沉积物可见分量数据集的yolov3算法的检测精度，本文提出了一种确定训练样本的新方法，可以提高培训样本的质量。然后，我们更改要融合层的卷积内核接收字段大小以增加检测精度。数据集上的实验结果表明，改进的yolov3具有较高的检测精度，其中5个类别的尿泥沉积物可见部件。结果表明，我们的方法获得了90.1％的最佳平均平均精度（地图），比原来的yolov3型号更高。该模型的平均检测时间为800×600分辨率为0.047s。

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