Improvement of Vehicle detection and classification performance with Region of Interest
Abstract
This Abstract-Paper presents an innovative approach to improve the performance of vehicle detection and classification in the context of computer vision. This study proposes the use of Region of Interest (ROI) to identify and process vehicle information in traffic images. The proposed method is based on a combination of deep learning-based object detection algorithms with a more focused ROI-based approach. First, we use state-of-the-art convolution neural networks to perform object detection in traffic images, which involves a visual recognition process to identify vehicle locations. Then, using ROI, we reorder and reduce the dimensions of irrelevant data to remove background areas and increase attention to vehicle objects. This aims to reduce the computational load and speed up the detection and classification processes. We evaluate the proposed approach on a wide traffic dataset and compare the results with conventional object detection approaches without using ROI. The experimental results show that the proposed method outperforms the conventional approach in terms of vehicle detection speed and accuracy. In conclusion, this study succeeded in improving the performance of vehicle detection and classification by leveraging Region of Interest, which allows better focus on relevant regions and reduces computational complexity. These results can make an important contribution to the development of a more efficient and reliable traffic control and transportation system.
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