# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 2 Related Work
The activation function in each convolution block is ReLU. Transformer features and CNN features are fused. The dehazed image is generated by the CNN decoder module. [PERSON] et al. [18] proposed FFA-Net, which is an end-to-end feature fusion network. There is no input of a clear image during training. The feature attention (FA) block combines the channel attention (uneven haze) and pixel attention (low intensity color channel). The training process adopted only an L1 loss function. [PERSON] et al. [19] proposed an autoencoder network with contrastive regularization for image dehazing. The idea was to generate a dehazed image closer to the ground-truthed image and further from a hazy image. The model, consisting of 2.61 M parameters, has a simpler structure than FFA-Net. [PERSON] et al. [20] proposed an image dehazing network based on U-Net. The decoder module was modified with the incorporation of a strengthen-operate-subtract (SOS) boosting strategy which generates the dehazed image progressively from the multi-scale features. However, as presented in [19], the network is far more complex than other models, such as AOD-Net and FFA-Net, with lower performance. GAN is a well-known CNN for image synthesis. Many researchers have adopted it for image-to-image translation, single-image dehazing, etc. However, GAN may suffer from training failure. To address this problem, cGAN was proposed, with constraints added to the GAN architecture. Some cGAN models have been proposed recently for single-image dehazing. For instance, [PERSON] et al. [21] proposed a prior guided cGAN framework which contains an encoder-decoder-based generator and a multi-scale discriminator. Features are extracted using an attention-based encoder and parameters are shared with the generator. [PERSON] et al. [22] proposed a cGAN framework, which adopts a U-shaped residual network as the generator. [PERSON] et al. [23] proposed the cGAN model based on the generator network with an encoder-decoder structure of the U-Net. They adopted the summation method to skip connections. The activation functions were ReLU and LeakyReLU. They evaluated the model only using a synthetic dataset. The performance on real degraded images is not known. We adopted the cGAN structure for our proposed image dehazing model. To strengthen the analytical power of the network, we propose two modifications. First, we designed the framework with convolutional blocks comprising more layers in the encoder output. We used the concatenation method to forward the detail features from the encoder to the decoder. According to [PERSON] et al. 's results [23], the PSNR of the concatenation method is higher than the summation method for most of the training epochs. Second, for the activation function in the generator and discriminator, we selected a new nonlinear activation function Mish. As will be explained in Section 3.1, Mish is better than ReLU in allowing gradient flow. We also inserted dropout layers in the generator to provide more variety of network configuration during training. To facilitate image dehazing research, many synthetic or real image datasets have been created. The acquisition of haze-free and hazy images, e.g., in the indoor environment, can be made under control. For instance, [PERSON] et al. [24] utilized a professional machine to generate haze in a scene. Therefore, clear and hazy image pairs can be captured. However, the acquisition of haze-free outdoor images is a tedious task. One possible approach is to add the hazy effect to a clear image via simulation. For instance, [PERSON] et al. created two synthetic datasets, the Foggy Road Image Database (FRIDA) [3] and FRIDA2 [25]. [PERSON] et al. [26] constructed two foggy datasets to facilitate foggy scene understanding and image dehazing. They applied fog synthesis on the Cityscapes dataset and generated Foggy Cityscapes with 20,550 images. Alternatively, [PERSON] et al. [27] created BeDDE, which contains real outdoor images. The haze-free and hazy image pairs were acquired in slightly different positions.