Tags:Classification, CNN, Hyperparameter tuning, Object Detection and YOLO
Abstract:
The deep learning models enhanced object detection in videos to a very large scale. Object detection is a prime task in self-driven cars, satellite images, robotics, etc. The researchers are working to improve the efficiency of deep learning models for better object detection. The object detection models broad categories into one-stage and two-stage detectors. The current work focused on improvement in accuracy and speed of one stage detector with the help of hyper-parameter tuning. The earlier researcher has shown that YOLO and R-CNN are the appropriate models for real-time object detection. In this paper, a custom CNN model is given with hyper-parameter tuning and the results are compared with regions with convolutional neural networks, Fast regions with convolutional neural networks, Faster Region with CNN, and YOLO. The impact of hyper-parameter tuning on the result of CNN models for object detection is shown in this paper. The results are verified on live video dataset.
Video Object Detection with an Improved Classification Approach