# Dist-YOLO: Fast Object Detection with Distance Estimation
### Related Work  
The existing methods can be split into two groups, predicting relative and absolute distance. The methods for estimating relative distance can express a particular object's (given by a bounding box) distance on a relative scale [7] or realize dense prediction and produce a depth map for the entire image. The depth map can be entirely independent between distinct images [8] or preserve the consistency [9]. We can view the depth map as a type of disparity map, where it can be produced using a single image only [10] or via multiple images [11]. The methods predicting absolute distance (in meters, inches, etc.) can be further separated into those that use data from active devices, passive devices, or their combination. Active devices are mostly represented by lidar, which has a better visibility range than sonar. VoxelNet [12] is trained in the end-to-end scheme over the lidar raw point cloud and can detect objects of car, cyclist, and pedestrian classes together with their precise 3D bounding boxes and position in a 3D scene. Study [13] proposed coupling two VoxelNets, one for region proposal and the second one for patch refinement, which led to a precision improvement on the KITTI 3D object detection benchmark [14; 15]. Regarding the combination of both types of devices, we can find models trained using lidar and visual data but perform inference using visual information only. [PERSON] et al. [16] separated an RGB image into small patches and by applying a probabilistic model produced a depth map close to the output of a 3D scanner. Generally, such approaches search for optimal mapping from the visual domain into a depth map, which is ideal for deep neural networks based on autoencoders [17]. Analysis [18] demonstrated that object-specific distance estimation using a monocular camera was not flawless, so they segmented Velodyne point clouds and fused them with features from a model to precisely predict the distance. Regarding passive devices, [PERSON] et al. [3] used three cameras with a small field of view and using a disparity calculation produced a dense depth map with reasonable results even for an object with distance higher than 200 m. The crucial point is to deal with pseudo-rectification and ambiguity removal. [PERSON] et al. [19] used a monocular camera and performed object detection and tracking, where the distance of an object was identified as valuable information for the tracker. The idea was based on Faster R-CNN enhanced by distance and angle estimation. Study [20] aimed at 3D box detection and an angle estimation network, which provided attitude angle information, and using the camera projection principle over this information, the distance was precisely determined. [PERSON] et al. [21] utilized YOLOv3 to detect bounding boxes together with coordinates, and from them, computed distances analytically. DisNet [2; 22] combined YOLOv3 that produced bounding box (BB) coordinates and a fully connected neural network that produced distances. The fully connected network was trained separately on predictions taken from YOLO. [PERSON] et al. [23] used YOLOv3 and coupled it with Monodepth [24], i.e., a model trained on visual information from two cameras to produce a disparity map on a single camera during inference. The distance was estimated from Monodepth's output and injected into YOLO's predicted boxes. [PERSON] et al. [25] used two cameras and two YOLOv3 detectors, where the distance was measured with a stereoscopic principle over the detected boxes. The disadvantages were the doubled computation time due to the two detectors and the impossibility of determining the distance when only one detector found an object. [PERSON] et al. [26] integrated into YOLOv3 a 3D regression module that took the detected boxes together with feature maps and utilized additional convolution layers to produce the orientation, dimension, and distance of each of the boxes.