Research on object detection algorithm based on convolutional neural network

With the advancement of deep learning frameworks, object detection algorithms based on deep learning have gained widespread adoption across various domains, including mechanical manufacturing, urban transportation, and medicine. Compared to traditional vision-based template matching methods, deep learning-based object detection methods exhibit lower requirements for object characteristics, superior generalization capabilities, and higher accuracy in detection. They are particularly suitable for detecting similar objects against complex backgrounds. This paper provides a comprehensive review of two-stage and one-stage object detection network architectures by summarizing their advantages and disadvantages. Additionally, it discusses the backbone networks used, training datasets employed, evaluation metrics utilized for comparing target detection algorithms' accuracy and response speed. Furthermore, it presents improved approaches for enhancing target detection algorithms' performance. Finally, this study proposes research focus areas and future development directions in the field of object detection.