Name
Abstract | ||
|---|---|---|
On purpose of aiding detection and recognition for railway infrastructure and dramatic changes in the environment around railways, visual inspection based on unmanned aerial vehicle (UAV) images is a highlight. However, UAV images often suffer from degradation for fog or haze, which limits the inspection efficiency. Most existing methods depend on a suboptimal two-step network with much more redundant procedures where transmission map and atmospheric light are estimated at first, and then haze-free images can be acquired using a dehazing model. This paper presents a novel end-to-end network for UAV-based railway images dehazing, and focuses on two key issues: network architecture and loss function. With regards to the first aspect, based on a pyramidal network structure, densely pyramidal residual network (DPRnet) consists of dense residual block and enhanced residual blocks, which heavily exploits the feature maps of all preceding layers and considerably increased depth at different scale, respectively. With regards to the second, a new loss function introducing structural similarity index is proposed to preserve more structural information, thereby restore the appealing perceptual quality of the hazy images. Finally, quantitative and qualitative evaluations illustrate that the DPRnet achieves better performance over the classic methods, yet remains efficient and convenient. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.06.076 | Neurocomputing |
Keywords | Field | DocType |
Images dehazing,UAV images degradation,Railway inspection,Image recognition,Densely residual network,SSIM | Residual,Visual inspection,Pattern recognition,Qualitative Evaluations,Residual Blocks,Network architecture,Artificial intelligence,Mathematics,Network structure,Haze | Journal |
Volume | ISSN | Citations |
371 | 0925-2312 | 1 |
PageRank | References | Authors |
0.39 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunpeng Wu | 1 | 144 | 5.60 |
| Yong Qin | 2 | 16 | 4.52 |
| Zhipeng Wang | 3 | 20 | 7.49 |
| Xiaoping Ma | 4 | 12 | 4.25 |
| Zhiwei Cao | 5 | 3 | 1.08 |