A Review on Techniques to Enhance the Accuracy and Efficiency of Target Detection

Abstract

Object detection plays a pivotal role in computer vision applications, with many methodologies constantly being developed to improve accuracy and efficiency. This paper presents an innovative approach that aims to enhance the accuracy of object detection through the use of more advanced and accurate methods. The proposed method leverages state-of-the-art techniques, including deep learning architectures, feature engineering, and optimization filter algorithms, to achieve superior results compared to existing methodologies. The study evaluates the performance of the proposed method using benchmark datasets and demonstrates its effectiveness in accurate object detection via the use of the Bayesian filter algorithm. The findings of this review highlight the potential impact of adopting more accurate object detection methods on advancing the capabilities of computer vision systems, paving the way for researchers in the field to improve applications in areas such as autonomous vehicles, surveillance, and robotics.