A Survey on Various Available Object Detection Models and Application In Automatic License Plate Detection


Students, Aditya Kulkarni, Manali Munot, Sai Salunkhe, Shubham Mhaske, Nilesh Korade, Assistant Professor
Pimpri Chinchwad College of Engineering and Research, Ravet, Pune, India.


With the development in technologies right from serial to parallel computing, GPU, AI, and deep learning models a series of tools to process complex images have been developed. The main focus of this research is to compare various algorithms(pre-trained models) and their contributions to process complex images in terms of performance, accuracy, time, and their limitations. The pre-trained models we are using are CNN, R-CNN, R-FCN, and YOLO. These models are python language-based and use libraries like TensorFlow, OpenCV, and free image databases (Microsoft COCO and PAS-CAL VOC 2007/2012). These not only aim at object detection but also on building bounding boxes around appropriate locations. Thus, by this review, we get a better vision of these models and their performance and a good idea of which models are ideal for various situations.