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Abstract
Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. Current models are designed for sharp images, leading to potential detection failures in blurry images. Using image deblurring before object detection is an option, but it demands significant computing power and relies heavily on the accuracy of the deblurring algorithms. Another common issue is the suitable dataset for the specific problem. To address the aforementioned issues, we develop a UAV-assisted small object detection dataset and propose a novel knowledge distillation method for object detection in blurry images in complex environments. Following this, we employ a technique known as self-supervised knowledge distillation, where we introduce a deblurring subnet module with the help of two attention modules, where both networks are trained in a fully-supervised manner. Based on the experiment results, our proposed model achieves an improvement of 4.3% accuracy in the VisDrone synthetic motion blur dataset and 4.6% in detecting objects within synthetic blurry images in our developed small object detection dataset (SOD-Dataset), as well as competitive results compared with other state-of-the-art methods. Meanwhile, ablation experiments and a visualization analysis validate the contributions of each component of the model.