Multi-Scale Vehicle Detection for Foreground-Background Class Imbalance with Improved YOLOv2
1
Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China
2
School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(15), 3336; https://doi.org/10.3390/s19153336
Received: 22 June 2019 / Revised: 26 July 2019 / Accepted: 26 July 2019 / Published: 30 July 2019
(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments)
Vehicle detection is a challenging task in computer vision. In recent years, numerous vehicle detection methods have been proposed. Since the vehicles may have varying sizes in a scene, while the vehicles and the background in a scene may be with imbalanced sizes, the performance of vehicle detection is influenced. To obtain better performance on vehicle detection, a multi-scale vehicle detection method was proposed in this paper by improving YOLOv2. The main contributions of this paper include: (1) a new anchor box generation method Rk-means++ was proposed to enhance the adaptation of varying sizes of vehicles and achieve multi-scale detection; (2) Focal Loss was introduced into YOLOv2 for vehicle detection to reduce the negative influence on training resulting from imbalance between vehicles and background. The experimental results upon the Beijing Institute of Technology (BIT)-Vehicle public dataset demonstrated that the proposed method can obtain better performance on vehicle localization and recognition than that of other existing methods.
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Keywords:
vehicle detection; YOLOv2; focal loss; anchor box; multi-scale
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MDPI and ACS Style
Wu, Z.; Sang, J.; Zhang, Q.; Xiang, H.; Cai, B.; Xia, X. Multi-Scale Vehicle Detection for Foreground-Background Class Imbalance with Improved YOLOv2. Sensors 2019, 19, 3336.
AMA Style
Wu Z, Sang J, Zhang Q, Xiang H, Cai B, Xia X. Multi-Scale Vehicle Detection for Foreground-Background Class Imbalance with Improved YOLOv2. Sensors. 2019; 19(15):3336.
Chicago/Turabian StyleWu, Zhongyuan; Sang, Jun; Zhang, Qian; Xiang, Hong; Cai, Bin; Xia, Xiaofeng. 2019. "Multi-Scale Vehicle Detection for Foreground-Background Class Imbalance with Improved YOLOv2" Sensors 19, no. 15: 3336.
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