Deep learning methods have been more and more widely applied in the field of target detection. As an important part of deep learning target detection, non-maximum suppression is used to eliminate redundant detection bounding boxes generated during target detection and find out the optimal target boundary boxes, so as to speed up detection efficiency and improve detection accuracy. This article first introduces the related concepts and computational principles of traditional non-maximum suppression algorithm, and points out its problems. Based on this, the Soft-NMS, Softer-NMS, IOU-Guided NMS, Adaptive NMS and DIOU-NMS, a total of 5 kinds of improved maximum suppression algorithm principle is introduced and comparative analysis. And then we summarize the advantages and disadvantages of various algorithms. Finally, in view of the common problems existing in each algorithm, this paper points out the direction for improvement of non- maximum suppression algorithm, and provides technical reference and support for researchers in related fields.
Aiming at the problem that the previous tubular target leak detection method is not sensitive to small leakage and slow leakage and false alarm caused by external interference is not strong, tubular target leakage detection method based on deep learning is proposed. Firstly, the target detection network YOLO3 model is used to detect the tubular target in optical images. By analyzing the test results, the YOLO3 based tubular target leakage detection network is optimized and improved from three aspects: data set expansion, detection mode and network structure. Including: 1) using data transformation using rotation transformation, color dithering, zoom transformation, shift transformation and flip transformation on the data set; 2) according to the characteristics of the tubular target images, the detection method of polygon frame selection is used; 3) simplifying the network structure of the detection and output part. Finally, the improved network is trained and verified. The experimental results show that compared with the YOLO3 network model, the recognition accuracy and recall rate of the tubular target and the leaked area are greatly improved, and the average detection time is also reduced.
The imaging of the image sensor is blurred due to the rotational motion of the carrier and reducing the target recognition rate greatly. Although the traditional mode that restores the image first and then identifies the target can improve the recognition rate, it takes a long time to recognize. In order to solve this problem, a rotating fuzzy invariants extracted model was constructed that recognizes target directly. The model includes three metric layers. The object description capability of metric algorithms that contain gray value statistical algorithm, improved round projection transformation algorithm and rotation-convolution moment invariants in the three metric layers ranges from low to high, and the metric layer with the lowest description ability among them is as the input which can eliminate non pixel points of target region from degenerate image gradually. Experimental results show that the proposed model can improve the correct target recognition rate of blurred image and optimum allocation between the computational complexity and function of region.