Disparity-Based Multiscale Fusion Network for Transportation Detection
Jing Chen, Qichao Wang, Weiming Peng, Haitao Xu, Xiaodong Li, Wenqiang Xu
Abstract
The transportation detection of long-distance small objects has low accuracy. In this work, we propose DMF, which is based on disparity depths. We map different disparity regions to 2D candidate regions according to the distance to solve the small-object detection problem. This method clusters disparity maps of different depths. The projected image is extracted with image features in the mapping region. On the one hand, it uses a multicluster method to unsample 2D mapping regions. On the other hand, the feature fusion of different scales is performed on each cluster region. The experimental results on two datasets show that DMF can improve the detection accuracy of small objects.