HazyDet: Open-source Benchmark for Drone-view Object Detection with Depth-cues in Hazy Scenes Publication date: 30 Sep 2024 Topic: Object detection Paper: https://arxiv.org/pdf/2409.19833v1.pdf GitHub: https://github.com/grokcv/hazydet Description: We introduce HazyDet, a large-scale dataset tailored for drone-based object detection in hazy scenes. It encompasses 383,000 real-world instances, collected from both naturally hazy environments and normal scenes with synthetically imposed haze effects to simulate adverse weather conditions. By observing the significant variations in object scale and clarity under different depth and haze conditions, we designed a Depth Conditioned Detector (DeCoDet) to incorporate this prior knowledge. DeCoDet features a Multi-scale Depth-aware Detection Head that seamlessly integrates depth perception, with the resulting depth cues harnessed by a dynamic Depth Condition Kernel module.