An examination of algorithms for target detection leveraging deep learning techniques

The swift advancement of AI technology has led to significant progress in computer vision through the implementation of deep learning. Recently, algorithms for target detection leveraging deep learning techniques have emerged as a significant focus of research in this domain due to their exceptional capability in feature extraction, finding application across various sectors such as agriculture, industry, and transportation. This study examines the progression of algorithms for target detection that utilize deep learning techniques, highlighting and evaluating the strengths and weaknesses inherent in both two-stage and single-stage detection methods, while also presenting and scrutinizing prominent areas of application in this domain. This study examines the present research landscape and anticipates future trends in deep learning-driven target detection methods, offering insights for ongoing exploration in this domain.