## 1 Introduction  
At present, China's coal mining industry has gradually progressed from mechanization and automation to intelligence, and the development goal of intelligent mines and unmanned pits has become the consensus of the industry. With unmanned mining activities, coal mine auxiliary transportation has become a bottleneck restricting unmanned coal mines, and it is frequently linked to sporadic, underground accidents. The auxiliary transportation system urgently needs to develop in the direction of standardization, intelligence, and being unmanned [1; 2; 3]. The coal mine rail locomotive is one of the main methods of auxiliary transportation, and the realization of an unmanned rail locomotives is of great significance for the development of coal mine intelligence. Considering that the underground workways in coal mines are not a completely closed working environments, obstacles, such as people, rail locomotives, and falling stones, sometimes appear on the transport line. The real-time and accurate detection of these obstacles is one of the key technologies of driverless rail locomotives. Compared with other on-board sensors, cameras can obtain the most abundant information about the surrounding environment. Moreover, with their low cost and highly developed hardware, cameras have unique advantages in the field of autonomous driving. However, a camera is susceptible to environmental factors. When the illumination changes or the background becomes complex, the accuracy and robustness of the target detection algorithm decreases. At present, the research on the intelligent-vehicle visual-object detection algorithm has made considerable progress; however, there are still a series of problems and difficulties in its application in underground coal mines. In general, the factors affecting the overall performance of the algorithm mainly include complex-scene and network-model factors in the coal mine: (1) complex-scene factors in a coal mine mainly include low illumination levels, motion blur, occlusion, small targets, background interference, etc. ; (2) network-model factors include an existing detection model with a complex architecture and many parameters, which cannot meet the requirements of the real-time detection of edge devices. On the premise of ensuring high detection accuracy, it becomes particularly important to improve the detection speed of the model. The visual equipment of the automatic rail locomotive is installed at the front of the locomotive, and the uneven track, operation of the rail locomotive, and movement of obstacles blur the pictures collected by the visual equipment, which seriously hinder the intelligent detection based on the image processing technology. It is necessary to deblur the collected pictures. With the rapid development of deep learning and big data technology, image deblurring using the Convolution Neural Network (CNN) has been widely studied. [PERSON] et al. [4] proposed a Generative Adversarial Network (GAN) in 2014, which relies on the idea of two network games to conduct targeted learning, and, on this basis, can be expanded to generate realistic and clear images to achieve the blind deblurring effect. [PERSON] et al. [5] proposed the deep convolution generative adversarial network in 2016, which was improved on the basis of GAN to address the defect of GAN learning instability. The generative model introduces the convolution network structure, which effectively improves the learning level of the network. [PERSON] et al. [6] proposed the SRGAN neural network in 2017. Based on the perceptual loss optimization algorithm, super-resolution image restoration was realized. [PERSON] et al. [7] proposed the Conditional Generative Adversarial Network (CGAN). Based on GAN, additional conditional information was added to the corresponding generator and discriminator to realize the conditional generation model. In 2018, [PERSON] et al.