TY  - JOUR
PY  - 2022//
TI  - Multi-object detection at night for traffic investigations based on improved SSD framework
JO  - Heliyon
A1  - Zhang, Qiang
A1  - Hu, Xiaojian
A1  - Yue, Yutao
A1  - Gu, Yanbiao
A1  - Sun, Yizhou
SP  - e11570
EP  - e11570
VL  - 8
IS  - 11
N2  - Despite significant progress in vision-based detection methods, the task of detecting traffic objects at night remains challenging. Visual information of medium and small stationary objects is deteriorated due to poor lighting conditions. And the visual information is important for traffic investigations. For meeting the needs of night traffic investigations, this study focuses on presenting a nighttime multi-object detection framework based on Single Shot MultiBox Detector (SSD). Considering the need of traffic investigations, the applicable detection framework is presented for detecting traffic objects, especially medium and small stationary objects. In the framework, the Dense Convolutional Network (DenseNet) and deconvolutional layers are introduced to enhance the feature reuse, and the effectiveness of the optimization is finally verified. In this paper, qualitative and quantitative experiments are presented. The results show that our presented framework has better detection performance for medium and small stationary objects. Moreover, the results show that presented framework has better performance for nighttime traffic investigations at intersections.<p />  <p>Language: en</p>
LA  - en
SN  - 2405-8440
UR  - http://dx.doi.org/10.1016/j.heliyon.2022.e11570
ID  - ref1
ER  -