JOINT OPTIMIZATION FOR OBJECT DETECTION IN FOGGY WEATHER CONDITIONS
Object detection using deep convolutional neural networks (CNN) has been widely studied and achieved impressive results in recent years. However, object detection in the presence of fog is far from solved because of poor visibility. In this paper, a novel CNN-based object detection model, named DFONet is introduced to address the problem of detecting objects in foggy weather conditions. DFO-Net is composed of two subnets including a defogging subnet and detection subnet. The defogging subnet is responsible for producing clean features from foggy images and sharing them with the detection subnet. The detection subnet uses these resulting features as the input and performs object classification and object localization. DFO-Net is trained end-to-end to jointly optimize visibility enhancement and object detection tasks. Experimental results on the FOD dataset indicate that the proposed DFO-Net obtained 48.85% mAP, outdoing many curent state-of-the-art object detection methods.
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