Plug-and-Play Joint Image Deblurring and Detection

Object detection is a widely researched topic in computer vision; however, current models often struggle with processing degraded images with adverse imaging conditions like low light, blur, and haze. Conventional approaches involve a separate image recovery network prior to detection, resulting in a large network that is sub-optimal in performance. Alternatively, in this study, we propose a lightweight plug-and-play solution to improve the performance of object detectors on degraded images, without the need for retraining the vision task, i.e., detector network. This solution utilizes an image enhancement plug-in subnetwork that can be turned on and off for the main vision task network, leading to improved detection accuracy without sacrificing inference time. Empirically, our proposed model achieved a 48.9% mean average precision (mAP) on a degraded Pascal VOC dataset, compared to the baseline model at 26.7%.