RepRCNN: A Structural Reparameterisation Convolutional Neural Network Object Detection Algorithm Based on Branch Matching

Abstract

A CNN object detection method based on the structural reparameterisation technique using branch matching is proposed to address the problem of balancing accuracy and speed in object detection techniques. By the structural reparameterisation of the convolutional layer in the object detection network, the amount of computation and the number of parameters in the network inference are reduced, the memory overhead is lowered, and the use of the branch-matching method to improve the structural reparameterisation model improves the computational efficiency and speed of the network while maintaining the detection accuracy. Optimisation is also carried out in terms of target screening and loss function, and a new CPC NMS screening strategy was introduced to further improve the performance of the model. The experimental results show that the proposed method achieves competitive results on the PASCAL VOC2012 and MS COCO2017 datasets compared to the traditional object detection methods and the current mainstream models, achieving a better balance between the detection accuracy and detection speed.