Object detection for small pixel in urban roads videos

Jin Yao; Zhang Rui; Yin Dong

doi:10.12086/oee.2019.190053

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Jin Yao, Zhang Rui, Yin Dong. Object detection for small pixel in urban roads videos[J]. Opto-Electronic Engineering, 2019, 46(9): 190053. doi: 10.12086/oee.2019.190053

Citation:

Jin Yao, Zhang Rui, Yin Dong. Object detection for small pixel in urban roads videos[J]. Opto-Electronic Engineering, 2019, 46(9): 190053. doi: 10.12086/oee.2019.190053

Object detection for small pixel in urban roads videos

1.
College of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China
2.
Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei, Anhui 230027, China

Fund Project: Supported by 2018 Anhui Key Research and Development Plan Project (1804a09020049)

More Information

Corresponding author: Yin Dong, E-mail:yindong@ustc.edu.cn

Received Date 30 January 2019

Revised Date 08 April 2019

Published Date 30 September 2019

Abstract

Abstract

Small pixel targets in video images are difficult to detect. Aiming at the small pixel target in urban road video, this paper proposed a novel detection method named Road_Net based on the YOLOv3 convolutional neural network. Firstly, based on the improved YOLOv3, a new convolutional neural network Road_Net is designed. Secondly, for small pixel target detection depending on shallow level features, a detection method of 4 scales is adopted. Finally, combined with the improved M-Softer-NMS algorithm, it gets higher detection accuracy of the target in the image. In order to verify the effectiveness of the proposed algorithm, this paper collects and labels the data set named Road-garbage Dataset for small pixel target object detection on urban roads. The experimental results show that the algorithm can effectively detect objects such as paper scraps and stones, which are smaller pixel targets in the video relative to the road surface.
- video image /
- smaller pixel object /
- convolutional neural network

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References

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Overview

Overview

Overview: Small pixel target detection is a kind of difficult program. Existing object detection benchmarks and methods mainly focus on standard detection task. However, these ways cannot get good performance on low-pixel ratio object detection, which has a few pixel in high resolution images. And the early target detection frameworks such as R-CNN, YOLO series are not very good for small pixel target detection. In order to solve this problem, this paper proposes an improved YOLOv3 network and the algorithm using M-Softer-NMS to improve the detection ability of small targets. Firstly, Road_Net convolutional neural network is proposed. YOLOv3's Darknet53 network is too complicated and redundant. What's more, too many parameters will bring difficulty in training, increase the requirements on the dataset, and reduce the speed of detection, which will not achieve better real-time performance. Accuracy and real-time performance are challenging in small object detection on urban roads. Therefore, we proposed a convolutional neural network Road_Net with relatively low computational complexity as a feature extraction network. Secondly, a detection method of 4 scales is used to more fully use shallow level features. In view of the fact that the targets in this context are mostly small pixel targets, the original three scale detections are extended to four scale detections, and the larger feature maps are assigned to the smaller pixel targets with more accurate anchor frames. Finally, M-Softer-NMS algorithm is used to further improve the detection accuracy of the target in the image. Softer-NMS is further improved after Soft-NMS. A new loss function (KL Loss) for bounding box regression is proposed to learn the bounding box transformation and positional reliability at the same time. Combined with the characteristics of small pixel targets in this paper, the M-softer-NMS algorithm for this paper is proposed based on softer-NMS. In order to verify the effectiveness of the algorithm, we collected and labeled the data set named Road-garbage Dataset for the detection of small pixel target objects on the road. The Dataset is based on several main roads in a certain city and selects 1200 different main roads in different regions. The experimental results show that the accuracy, recall rate and AP can reach 95.29%, 91.12% and 82.41% respectively, while real-time detection is 57.9 f/s. In the next work, we will continue to improve the network and optimize the algorithm for higher accuracy and lower time cost, and continue to capture and use our more realistic scene images to expand our dataset for better application.