# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 2 Related Work
A quantitative measure was computed on the common region of interest (ROI) of the image pair which was manually segmented by the authors. As we did not a find haze-free and hazy real road scene image pair dataset, we collected and annotated real road scene videos for our research. The degraded images were synthesized through the addition of rain and fog effects to the clear images. Deep learning-based object detection methods can be grouped into two categories--one stage and two stage. A one-stage detector conducts target classification and target positioning in one pass. For instance, YOLO [28] directly calculates the position and category of objects in the output layer. SSD [29] uses a multi-scale feature map to return the location and category of the objects. A two-stage detector produces a target bounding box first. Then, for each candidate box, classification and regression are carried out. For instance, R-CNN [30] adopts the region proposal method to generate the ROIs. The ROIs are then converted into fixed-size images and fed into the CNN to achieve target classification and refinement of the bounding box. Faster R-CNN [31] extracts the image feature only once, instead of extracting a feature for each ROI. It achieves target classification and refinement of the bounding box based on ROI pooling which converts each of the feature maps with various sizes into a fixed-size feature map. Mask R-CNN [32] performs instance segmentation. It is a two-stage method that divides instance segmentation into object detection and mask representation. It adopts ResNet [33] as the backbone to extract feature maps. A pyramidal network is used to combine the feature maps from the low layer to the high layer and produce the prediction feature maps. [PERSON] et al. [34] proposed a Faster R-CNN-based object detector with the addition of image-level adaptation, instance-level adaptation and consistency regularization of the two domain classifiers. The challenges are that the target domain has no annotation and its distribution is different from the source domain. The augmented model can tackle both image-level and instance-level shifts. The authors evaluated the proposed model on various datasets with different domain shift scenarios. For instance, Cityscapes was used as the source domain, while Foggy Cityscapes was used as the target domain. [PERSON] et al. [35] also tackled the domain adaptation problem with two modules, DQFA (to reduce domain discrepancy in global feature representation) and TDA (to reduce the domain gaps in instance-level feature representation), which were added to the backbone Deformable DETR. They evaluated the proposed unsupervised domain adaptive object detector (DAOD) on three scenarios, e.g., Cityscapes to Foggy Cityscapes. In summary, a two-stage detector can achieve higher accuracy (e.g., 0.7 mean average precision (mAP)) at the expense of higher computational load (e.g., 0.1 per image). A one-stage detector has a faster speed (e.g., 50 frames per second (fps)) with a slightly lower accuracy (e.g., 0.6 mAP). The accuracy is further decreased with the use of degraded images. In order to improve the object detection accuracy of the one-stage object detector, we utilized the proposed image dehazing method to pre-process the images.