Improved YOLO model with multi-feature fully convolutional network for object detection

The main task of object detection is to identify and locate interested objects from still images or video sequences. It is one of the key tasks in the field of computer vision. However, the object usually has variable factors in brightness, shape, occlusion and so on, and is interfered by various and complex environmental factors, which makes the research opportunities and challenges of object detection algorithm coexist. In this paper, a main frame of object detection algorithm based on convolutional neural network is studied, which is based on regression. We propose a real-time object detection algorithm based on fully convolution network, which aims to solve the problems of low detection accuracy and poor location accuracy of objects in regression method. The innovation is that the proposed fully convolution network increases the detection flexibility of the model because it is not affected by the input scale. At the same time, we propose a multi feature fusion and multi border prediction strategy, which effectively improves the detection accuracy of small objects. In order to prove the effectiveness of the proposed algorithm, we use PASCAL VOC data set to carry out object detection experiments. In this paper, the accuracy of each object category and the average accuracy of all categories are calculated. Experiments show that the performance of the multi feature fusion algorithm based on the fully convolution network is better than that based on the regression idea such as YOLO, and more than 10% higher than that of the YOLO model.