## 2 Related Work
### Limitations and Study Trends of Object Detection during Self-Driving  
The core technologies of self-driving are divided into the recognition, determination, and control phases [20]. In the recognition phase, the multiple sensors within the self-driving vehicle are utilized to precisely identify the near and far objects. Once the objects are recognized, in the determination phase, a high-quality self-driving mechanism is implemented through the human-level surrounding environment identification and road driving learning processes. In the final control phase, the control system of the self-driving vehicle is operated based on the recognition and determination results, and actions suitable for that particular situation are taken. At this point, in the process of understanding the road traffic situation and surrounding facilities, object detection technology using deep learning is applied. The recognition process using object detection during self-driving results in different actions on the road depending on the performance differences [21]. This is a really important process, since it has a direct influence on road safety. Accordingly, a number of studies are being actively conducted to improve the object recognition and detection performances. [PERSON], [PERSON] et al. [22] proposed an optic flow estimation algorithm based object detection and tracking process by taking into consideration the moving direction and size of the feature points of objects among frames in the image input phase. This model uses a median filter to effectively remove diverse types of noises in a traffic situation. However, since the involved study is a process based on the image sequence of static camera images, it is difficult to detect the prompt changes made in a continuously and dynamically changing self-driving environment. In addition, since the occluded objects were not taken into consideration in the process of detecting, filtering and dividing the objects of interest from the static image sequence, the model was limited in that the detection performance may be significantly degraded when the objects of interest are partially occluded by other objects. In the process of using the median filter, occlusion objects are likely to be judged as noise, and there is a point to be improved that occlusion objects do not properly detect them as objects of interest. During real-life road driving, the occlusion phenomenon frequently occurs and serves as the main factor that degrades the object detection performance. Figure 2a,b are road driving images included in the IARA data set and in the GOPRO data set, respectively, and shows examples of the occlusion phenomenon [23; 24]. Figure 2a shows the occlusion phenomenon where it is difficult to detect the crosswalk, because it is occluded by the vehicles in front. Figure 2b shows the occlusion phenomenon where the crosswalk located at the center is occluded by the shadow of the vehicles in front. In addition, due to the limited size of the image input from the self-driving vehicle, the crosswalk located to the right is cut and only partially shown, making it an occluded object that is difficult to detect. As described, in a self-driving situation, there is a problem that occluded objects that are difficult to detect occur depending on diverse  
Figure 2: Occlusion by various road driving environments. road environmental conditions such as climate and traffic volume. [PERSON], [PERSON] et al. [25] released a pedestrian detection survey to consider occlusion. The results of the survey show that modern method-based algorithms using deep learning are more powerful than conventional method-based detection algorithms that manually select pedestrian features. Nevertheless, existing deep learning-based object detection algorithms to solve occlusion problems have several issues to improve. The first is the training data problem, which, when the amount of data is small, does not provide a good detection effect with current algorithms.