Object detection in convolution neural networks using iterative refinements

One of the major problems in computer vision is the object detection through image classification. In recent years, the convolutional neural network (CNN) has been extensively applied for image classification. This paper combines iterative refinement and joint score function into a strategy to accurately localize the detected objects. First, a unified model with fast approximation was proposed to correct the position and range of region proposals, which are often incorrect in conventional linear methods. Focusing on data, the model can acquire knowledge without any cost, and suit different CNN architectures for various datasets. Next, a joint score function was introduced to process the number of candidate regions in the images. The joint score function deals with the relative position of the occluded object, and depends on the image data and output loss. Experimental results show that the proposed strategy achieved a 3.6% higher mean precision than the contrastive method. The research greatly promotes the object detection accuracy in computer vision. © 2019 International Information and Engineering Technology Association. All rights reserved.