Learning Deformable Network for 3D Object Detection on Point Clouds

Abstract

3D object detection based on point cloud data in the unmanned driving scene has always been a research hotspot in unmanned driving sensing technology. With the development and maturity of deep neural networks technology, the method of using neural network to detect three-dimensional object target begins to show great advantages. The experimental results show that the mismatch between anchor and training samples would affect the detection accuracy, but it has not been well solved. The contributions of this paper are as follows. For the first time, deformable convolution is introduced into the point cloud object detection network, which enhances the adaptability of the network to vehicles with different directions and shapes. Secondly, a new generation method of anchor in RPN is proposed, which can effectively prevent the mismatching between the anchor and ground truth and remove the angle classification loss in the loss function. Compared with the state-of-the-art method, the AP and AOS of the detection results are improved.

Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT

“…Reference [27] proposed a target detection method based on improved GUPNET. Te experimental results show that the average accuracy of the proposed method is the highest, with an average accuracy of 0.919 in simple difcult scenes, 0.897 in medium difcult scenes, and 0.839 in difcult scenes; reference [27] has an average accuracy of 0.900 in simple difcult scenes, 0.856 in medium difcult scenes, and 0.802 in difcult scenes; the average accuracy of reference [26] is the lowest, with the average accuracy of only 0.896 in simple difcult scenes, 0.879 in medium difcult scenes, and 0.783 in difcult scenes. Tis is because the proposed method divides the process of power operation violation recognition into two stages.…”

“…In order to prove the performance of the proposed method, it is compared with the methods in reference [26] and reference [27] under the same experimental conditions. Te comparison results are shown in Table 2.…”

“…Te comparison results are shown in Table 2. Reference [26] introduces deformable convolution into the point cloud target detection network to enhance the adaptability of the network to detection targets in diferent directions and shapes. Reference [27] proposed a target detection method based on improved GUPNET.…”

“…In order to analyze the impact of each classifer on the speed and memory consumption of the method, under the experimental conditions of moderately difcult scenes, it is compared with the methods in reference [26] and reference [27], and the comparison results are shown in Figure 6. Te memory occupation of the method in reference [26] is 4504 MB, and the running time is 399.7 ms. Te memory occupation of the proposed method is only 1687 mb, and the running time is 200.3 ms. Tis is because in the point cloud data preprocessing stage, the point cloud data is fltered and smoothed, which reduces the density of the point cloud and greatly reduces the amount of data, but its shape characteristics and spatial structure information can remain basically unchanged. Te processing of point cloud data by the comparison method is not perfect, takes up a lot of computer memory, and the calculation time is long.…”

“…Te model removes a large number of invalid point clouds through the fltering algorithm and then adds texture information through point cloud coloring to enhance features. Reference [26] introduces deformable convolution into the point cloud target detection network to enhance the adaptability of the network to detection targets in diferent directions and shapes. Aiming at the imprecision of target position and the uncertainty of foreground target depth distribution, reference [27] proposes a target detection method based on improved GUPNET.…”