Multi-Task Learning for UAV Aerial Object Detection in Foggy Weather Condition

Abstract

Adverse weather conditions such as haze and snowfall can degrade the quality of captured images and affect performance of drone detection. Therefore, it is challenging to locate and identify targets in adverse weather scenarios. In this paper, a novel model called Object Detection in a Foggy Condition with YOLO (ODFC-YOLO) is proposed, which performs image dehazing and object detection jointly by multi-task learning approach. Our model consists of a detection subnet and a dehazing subnet, which can be trained end-to-end to optimize both tasks. Specifically, we propose a Cross-Stage Partial Fusion Decoder (CSP-Decoder) in the dehazing subnet to recover clean features of encoder from complex weather conditions, thereby reducing the feature discrepancy between hazy and clean images, thus enhancing the feature consistency between different tasks. Additionally, to increase the feature modeling and representation capabilities of our network, we also propose an efficient Global Context Enhanced Extraction (GCEE) module to extract beneficial information from blurred images by constructing global feature context long-range dependencies. Furthermore, we propose a Correlation-Aware Aggregated Loss (CAALoss) to average noise patterns and tune gradient magnitudes across different tasks, accordingly implicitly enhancing data diversity and alleviating representation bias. Finally, we verify the advantages of our proposed model on both synthetic and real-world foggy datasets, and our ODFC-YOLO achieves the highest mAP on all datasets while achieving 36 FPS real-time detection speed.