Object detection involves the precise and efficient identification and localization of multiple predefined object categories within images. With the advent of deep learning, both the accuracy and efficiency of object detection have significantly improved. Nevertheless, challenges remain in optimizing the performance of mainstream detection algorithms, improving the accuracy of small object detection, enabling multi-class detection, and developing lightweight models. In response to these challenges, this paper provides a comprehensive literature review, analyzing approaches to enhance mainstream object detection by exploring advancements in backbone networks, expanding the visual receptive field, feature fusion, and various training strategies. We also evaluate the performance of leading detection models across established datasets, identifying current limitations and proposing future research directions. These directions include enhancing small object representation in datasets, enriching semantic information, and improving model interpretability. Small object detection remains a critical focus in computer vision, and we anticipate the continued development of algorithms with higher accuracy and efficiency
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