Collaboration of Dehazing and Object Detection Tasks: A Multitask Learning Framework for Foggy Image

Under foggy conditions, the atmospheric scattering effect diminishes the illumination intensity in images, causing a decline in the contrast of remote sensing images, which impacts the accuracy of object detection algorithms. Mainstream algorithms typically employ image restoration methods as a preprocessing step before object detection; however, the distributed method increases the time taken by the model to perform inference and it is difficult to ensure the dehazing module has positive effects on the object detection network modeling. To this end, we propose a multiscale feature collaborative foggy image object detection network (MS-FODN) within a multitask learning framework. This end-to-end model integrates a dehazing module to assist in object detection. In the proposed network, the encoder parameters of both the dehazing and detection modules are shared during the encoding stage. For the decoding stage, the dehazing module employs the U-Net architecture with a multiconstrained loss function to enhance dehazing, while the detection module adopted the DiffusionDet decoder as its detection head. To address the challenge of substantial scale differences in remote sensing targets, we proposed a multiscale attention feature fusion module that refines and calibrates features at different scales and aggregates them through the feature pyramid module (FPM). In addition, we implemented a multiscale KL loss optimization to minimize the negative impacts on object detection outcomes caused by the suboptimal performance of the dehazing decoder. Given the absence of open-source foggy aerial datasets, we developed a real remote sensing object detection dataset, RH-Vehicle, captured by UAVs. MS-FODN has been proven to exceed recent state-of-the-art methods in detection accuracy through extensive experiments on both simulated and real datasets.