# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 1 Introduction  
Many image-processing applications perform localization of target objects in the first place. For instance, detecting and inferring objects (such as road signs, pedestrians and vehicles) in road scene videos are the essential tasks of an autonomous driving system [1]. Representative features are estimated from image pixels, generally under the assumption that the image is acquired with good visibility. High quality images are also important for remote sensing and surveillance. For instance, ground settlement monitoring in construction sites, or structural health assessments of buildings, demand an accurate survey and geometric reconstruction of the scene. [PERSON] et al. [2] employed time series synthetic-aperture-radal interferometry (InSAR) to estimate the deformation of land reclamation. Remote sensing can also be achieved through the use of image processing techniques such as structure from motion (SfM) photogrammetry. One main problem of image-based applications is that the acquired images are of low quality. Remote sensing images are often captured in outdoor environments. The visibility in the images depends on the weather conditions. When there is haze, rain or fog, the acquired images are seriously degraded. As a result, the image feature is distorted and the image-processing algorithm will fail to reach the expected accuracy. To address this problem, image dehazing is often employed. Many image processing algorithms assume that the image records the scene's radiance. If the image dehazing method restores the image radiance, representative features can be extracted using the subsequent processing steps of the algorithm. Image dehazing is an important and foremostmodule in many applications, e.g., auto-driving [3], scene surveillance [4] and remote sensing [5]. These studies demonstrated that image dehazing can address some common problems faced by various image-based applications. Image dehazing has become a popular research topic. The amount of image dehazing papers has increased over recent years (see a recent review [6]). We aim to propose an image dehazing algorithm that can improve the visibility in images, particularly images that are degraded by rain and fog. Moreover, we demonstrate the benefit of using the proposed method on a popular image-based application of object detection. We propose a new image dehazing model based on a convolutional neural network (CNN). The single network can be trained end-to-end to transform a hazy image into a haze-free image. A quantitative evaluation of the proposed model, with respect to some metrics, was performed on various image datasets. As demonstrated from the numerical results, our model outperformed other image dehazing methods. Moreover, the visual results illustrated the improvement of visibility in the degraded images. The dehazed images look very similar to the haze-free images. In the research of object detection, we observed that outdoor images degraded by rain and fog produced predictions with lower accuracy than those obtained from clearer images. We, therefore, utilized the proposed image dehazing method to enhance the visibility of the degraded images before the object detection task. We investigated the impact of dehazed images with respect to the detection of five target objects captured in road scene videos. Following thorough experimentation, we demonstrated that the objects detected in images enhanced by our proposed dehazing model were significantly improved over those detected in the degraded images. Our main contributions are as follows:  
* We adopt the conditional generative adversarial network (cGAN) and propose a novel image dehazing model. The network, which is comprised of a generator and discriminator, demands no pre-processing step. Therefore, the single network learns the dehazing function via end-to-end training. The generator module has an encoder-decoder structure. We strengthened the analytical power of the network via the adoption of convolutional blocks with progressively more layers.