Vehicle Instance Segmentation Polygonal Dataset for Private Surveillance System

Abstract

Vehicle identification is an important task in traffic monitoring because it allows for efficient inference and provides a cause for action. Vehicle classification via deep learning and other approaches such as segmentation is a critical tool for re-identification. In this paper, instance segmentation is used to identify vehicle makes with license plate detection, allowing for better unique vehicle recognition for re-identification. A dataset is annotated and modified, for example, by segmenting it with polygonal bounding boxes that capture the vehicle's unique frontal features. In addition, license plate localization is performed. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches based on CNN and deformed CNN. Furthermore, a deep residual network and fully connected layer-based classification were utilized as the backbone for feature representation. Instance segmentation detects objects by segmenting and classifying regions of interest. The imbalance in the dataset is resolved using a mosaic-tiled approach, which produces greater precision than other approaches.

Faster RCNN based robust vehicle detection algorithm for identifying and classifying vehicles

“…As a result, numerous computer vision and machine learning models are thoroughly investigated in this study in order to solve a range of fascinating challenges in intelligent transportation systems. Researchers have suggested various classic vehicle detection algorithms from the earliest stages of the field to current days [9][10][11][12][13][14][15]. The performance of techniques is determined by handcrafted characteristics.…”