a survey of deep learning-based object detection: application and open issues

Object tracking and detection are among the most significant jobs in computer vision, having many applications in areas, which includes autonomous vehicle tracking, robotics, as well as traffic monitoring. several studies have been conducted in past years. however, since detecting various problems, for instance, fast motion, illumination variations, as well as occlusion, study in this field persists. furthermore, deep convolutional neural networks (dcnns) have grown increasingly significant for object detection as deep learning (dl) techniques have advanced. as a result, numerous approaches for object detection are studied in this research, as well as a comprehensive. this project encompasses backbone networks, loss functions and training strategies, classical object detection architectures, complex problems, datasets and evaluation metrics, applications, future development directions, as well as a review and analysis of dl-based object detection techniques conducted in previous years. experts in the field of object detection will benefit from this review article.