In order to solve the multi-vehicle mutual occlusion problem encountered in 3D target detection by self-driving vehicles, this paper proposes a monocular 3D detection method that includes dynamic occlusion determination. The method adds a dynamic occlusion processing module to the CenterNet3D network framework to improve the accuracy of 3D target detection of occluded vehicles in the road. Specifically, the occlusion determination module of the method uses the 2D detection results extracted from target detection as the occlusion relationship determination condition, wherein the method of changing the occlusion determination threshold with the depth value is introduced. Then the occlusion compensation module is used to compensate and adjust the 3D detection results of the occurring occluded vehicles, and finally the 3D target detection results are output. The experimental results show that the method improves the accuracy of both vehicle center point detection and 3D dimensional detection results in the case of long-distance continuous vehicle occlusion. And compared with other existing methods, the accuracy of 3D detection results and bird's-eye view detection results are improved by 1%-2.64% in the case of intersection over union of 0.5. The method can compensate for the occluded vehicles in 3D target detection and improve the accuracy
Nowadays pointer meters are widely used in industries. In this paper, we propose a novel automatic reading method for pointer meters. Different from previous works that adopt two-stage detectors for detection, our approach integrates two components: a one-stage detector NeighborNet (Neighbor Network) and an angle reading module. NeighborNet is a simple but effective framework that can generate keypoint detection and meter detection with higher accuracy and less inference time. Specifically, we construct a neighborhood module, which significantly improves the accuracy of keypoint detection, thereby improving the accuracy of meter readings. The prediction generated by NeighborNet are then delivered into the angle reading module to obtain the final meter reading. Experiments validate the effectiveness of our automatic reading method and also prove that our method is robust to different types of meters.
In this paper, we propose a novel detection-free framework for cabinet switch state recognition. Different from prior works which adopt object detection or detection followed by per-switch recognition, our approach processes the image as a whole. Specifically, a semantic segmentation model is used to generate a coarse semantic map as a temporary result, which will be further refined by an object counting module. Moreover, for higher efficiency, we augment the Fully Convolutional Network (FCN) by introducing a hierarchical feature aggregation structure, forming a lightweight yet effective model called HFA-FCN. The experimental results show that the proposed pipeline outperforms those based on object detection, especially in hard cases: images are low-quality, targets are densely distributed or distorted. To the best of our knowledge, we are the first to formulate the task of switch state recognition as a task of semantic segmentation and object counting.
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