## 2 Related Work
### YOLO-Based Methods
Moreover, the cost matrix of each stage is resolved using the Hungarian algorithm. Only detection information is used in the previous so as to achieve rapid execution. In [30], Faster-RCNN is tuned to detect vehicles in various scenarios. Moreover, this work also uses basic image processing methods along with morphological operations and multiple thresholding to achieve vehicle exact location in near-real-time. In [31], vehicle and distance detection method is developed in a virtual environment. This work mainly uses the Yolo v5s neural network structure and develops a novel neural network system, which the authors refer as the Yolo v5-Ghost. In the discussed approach, the authors further fine-tuned the network layer structure of the Yolo v5s. Experiments performed therein indicate that this method is suitable to be deployed in real-time environments. The authors of this work also claim that their work is suitable for embedded and edge devices and object detection in general [32]. In [33], a novel bounding box regression loss approach is developed that learns objects bounding box through miscellaneous transformations and variance localizations. The learned localization variance is further merged during non-maximum suppression that increases the localization performance. In [34], a dynamic vehicle detection method, which is based on a likelihood-field-based model and on Coherent Point Drift (CPD), is developed. This study also applies an adaptive thresholding on the distance and grid angular resolutions to detect the moving vehicles. This work also presents the pose estimation that is based on the CPD to estimate the vehicle pose. The scaling series algorithm is also coupled with a Bayesian filter to update the vehicle localization states during various intervals. In [35], a new Multi-Level Feature Pyramid Network (MLFPN) is proposed that constructs effective feature pyramids to detect objects. This method initially fuses multi-level features and later feeds the base features into a block of alternating joint thinned U-shape networks. Meanwhile, the decoder layers are gathered up with correspondent sizes to build a feature pyramid for object detection. In [36], the proposed method is primarily based on Trident Network (TridentNet), which aims to generate scale-specific feature maps. This scheme also constructs parallel multi-branches in which each branch shares the same transformation parameters. This algorithm also adopts a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. The proposed TridentNet achieves significant improvements without any additional parameters. In [37], a single-stage method uses Mask SSD to investigate objects. This work uses a convolutional series to predict pixelwise objects' separation. This work also optimizes the whole network through multitask loss function. Ultimately, the network directly predicts final objects presence results. This work also uses multi-scale and feedback features that perform well on various objects of different scales and aspect ratios. In [38], the developed method uses two classifiers to tackle the problem of failure to locate vehiclesthat have occlusions or slight interference. It accomplishes vehicle detection through a local binary pattern along with a support vector machine. This method also uses the CNN in the second phase to remove the interference areas between vehicles and any moving object. In [39], a novel CornerNet is developed achieve accurate object detection. The CornerNet approach detects objects bounding box as a pair of keypoints. The top-left corner and the bottom-right corners are localized through a single CNN. Through an intelligent paired keypoints approach, this method eliminates the need to design a set of anchor boxes that are normally used in prior single-stage detectors. This work also introduces corner pooling, which is a new type of pooling layer and helps the network to better visualize and localize the objects' corners.