Analysis of the Influence of De-hazing Methods on Vehicle Detection in Aerial Images
Khang Tan Tran Minh Nguyen, Phuc Nguyen, Doanh C. Bui, Minh Tam Tran, Nguyen D. Vo · International Journal of Advanced Computer Science and Applications · 2022
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.