# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 2 Previous Work
### Object Detection in Occluded Scenes  
Object detection is one of the fundamental problems in computer vision. In recent years, significant advances have been made in this field. Deep neural networks have revolutionized this field, starting with region-based methods [7; 8; 9]. Then, single-shot detectors, such as SSD or YOLO, provided significantly more efficient detectors [10; 11; 12], with their more advanced variants [13; 14; 15] achieving SOTA results on standard object detection tasks. Transformer-based methods [16] have also made advances recently, while, finally, anchor-free methods, such as CenterNet [17], aim to solve the problems of post-processing steps, such as Non-Maximum Suppression (NMS). Still, mostf these detection methods do not directly address the problem of detecting input disturbances, especially partial occlusion, in a supervised manner. Instead, most works focus on improving their results when occlusion is present without explicitly detecting it. This is partially due to the fact that, to the best of our knowledge, no large-scale datasets exist, where occlusion labels (e.g., masks defining the exact overlap area between two object silhouettes) are available, making direct supervised learning of occlusion maps infeasible. Notably, two datasets that contain partially occluded objects have been released recently. These sets of images with occluded objects and the adhering ground truth masks, describing the regions of the individual objects, are entitled the Occluded COCO [18] and OVIS datasets [19]. However, these datasets do not contain occlusion masks, i.e., the exact overlap area between two object silhouettes. This property limits the usability of both datasets for machine learning-based occlusion map detection. Object detection methods that aim to deal with partial occlusion need to rely on some form of self-supervised learning, meaning that the labels have to be generated automatically [20]. The most straightforward way of doing this is to generate synthetic occlusion in natural images, and train neural network-based methods to make correct predictions in spite of these artificial occlusions. Examples of this approach include [PERSON] et al. [3], where a semantic segmentation method is trained to predict the full object segments correctly, despite parts of the image being masked out. [PERSON] et al. [21] presented a generative approach. They trained a Generate Adversarial Network (GAN) to accurately restore masked-out parts of an image, thus, successfully restoring information lost by occlusion. While the performance of these methods is superb, unfortunately, the use of synthetic occlusions introduces a critical weakness. Since the distribution of these masked-out image parts is vastly different from the distribution of a natural image, detecting the occluded parts becomes significantly easier, and, thus, the generalization capabilities of these methods in regard to natural occlusions are questionable. This is not the case for Compositional Neural Networks (CompNets) [4; 5], which are one of the most notable methods of object detection under partial occlusion. This method employs unsupervised learning to learn the distribution of occluding parts and distinguish these from the distributions of parts containing relevant objects. This allows the method to be trained in natural images containing natural occlusions, thus, avoiding the aforementioned problems. While unsupervised learning of partial occlusion is impressive, we argue, however, that using supervised learning from annotated data might lead to superior performance. In a previous work, [PERSON] [22] used a simulation created via the Unity game engine to render realistic images with natural occlusion. They also used the game engine to extract weak occlusion labels for training. The intersections of the object bounding boxes were labeled as occlusion.