Wei Li

TL;DR: A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.

TL;DR: The impressive results indicate that vehicle detection and counting can be mutually supportive, which is an important and meaningful finding.

TL;DR: A novel deep learning based approach to detect the splashed water, which outperforms the state-of-the-art methods and is the first work on this topic based on deep learning.

TL;DR: Wang et al. as mentioned in this paper proposed a well-designed Vehicle Counting Network (VCNet) to alleviate the problem of scale variation and inconsistent spatial distribution in congested traffic scenes, which is composed of two major components: (i) to capture multiscale vehicles across different types and camera viewpoints, an effective multi-scale density map estimation structure is designed by building an attention-based mask refinement module.

TL;DR: Zhang et al. as discussed by the authors proposed a dense vehicle detection method based on vehicle counting perception attention, which characterized in that vehicles in a dense environment are detected based on a deep learning network model, the network model comprises a vehicle number perception network model and a dense target detection network model.