A Review of Research on Object Detection Algorithms

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Object detection is a fundamental task in computer vision, widely used in fields such as autonomous driving, security surveillance, medical imaging, and drone image analysis. With the continuous advancement of technology, object detection algorithms have evolved from traditional methods to deep learning approaches. This paper categorizes object detection algorithms into four types based on their technical characteristics and implementation methods: two-stage algorithms, one-stage algorithms, keypoint-based algorithms, and emerging Transformer-based methods. Through a performance comparison on existing datasets, it was found that two-stage algorithms excel in accuracy but consume significant computational resources, leading to slower speeds; one-stage algorithms have a clear advantage in speed but show lower accuracy in detecting small objects; keypoint-based methods effectively balance speed and accuracy; additionally, the emerging Transformer-based methods perform well in capturing global information but require large amounts of training data and computational resources. This paper summarizes the advantages and disadvantages of each type of algorithm and discusses future research directions.