Abstract: In recent years, object detection from space in adverse weather, incredibly foggy, has been challenging. In this study, we conduct an empirical experiment using two de-hazing methods: DW-GAN and Two-Branch, for removing fog, then eval-uate the detection performance of six advanced object detectors belonging to four main categories: two-stage, one-stage, anchor-free and end-to-end in original and de-hazed aerial images to find the best suitable solution for vehicle detection in foggy weather. We use the UIT-DroneFog dataset, a challenging dataset that includes a lot of small, dense objects captured in various altitudes, as the benchmark to evaluate the effectiveness of approaches. After experiments, we observe that each de-hazing method has different impacts on six experimental detectors.

Khang Nguyen, Phuc Nguyen, Doanh C. Bui, Minh Tran and Nguyen D. Vo, “Analysis of the Influence of De-hazing Methods on Vehicle Detection in Aerial Images” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01306100

@article{Nguyen2022,
title = {Analysis of the Influence of De-hazing Methods on Vehicle Detection in Aerial Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01306100},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01306100},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Khang Nguyen and Phuc Nguyen and Doanh C. Bui and Minh Tran and Nguyen D. Vo}
}