Light-Weight Mixed Stage Partial Network for Surveillance Object Detection with Background Data Augmentation

Chen Ping-Yang, Jun-Wei Hsieh, Munkhjargal Gochoo, Yong-Sheng Chen

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

State-of-the-art (SoTA) models have improved object detection accuracy with a large margin via convolutional neural networks, however still with an inferior performance for small objects. Moreover, these models are trained mainly based on the COCO dataset, and its backgrounds are more complicated than road environments, and thus degrade the accuracy of small road object detection. Compared with the COCO dataset, the background of a surveillance video is relatively stable and can be used to enhance the accuracy of road object detection. This paper designs a computationally efficient mixed stage partial (MSP) network to detect road objects. Another novelty of this paper is to propose a mixed background data augmentation method to enhance the detection accuracy without adding new labelling efforts. During inference, only the input image is used to detect road objects without further using any subtraction information. Extensive experiments on KITTI and UA-DETRAC benchmarks show the proposed method achieves the SoTA results for highly-accurate and efficient road object detection.

原文English
主出版物標題2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
發行者IEEE Computer Society
頁面3333-3337
頁數5
ISBN(電子)9781665441155
DOIs
出版狀態Published - 2021
事件2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
持續時間: 19 9月 202122 9月 2021

出版系列

名字Proceedings - International Conference on Image Processing, ICIP
2021-September
ISSN(列印)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
國家/地區United States
城市Anchorage
期間19/09/2122/09/21

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