Learning Object Detection using Multiple Neural Netwoks

Multiple neural network systems have become popular techniques for tackling complex tasks, often giving improved performance compared to a single network. In this study we propose an innovative detection algorithm in image analysis using a multiple neural network approach where many neural networks are jointly used to solve the object detection problem. We use a group of networks configured with different parameters and features, then combines them in order to obtain new networks. The topology of the set of neural networks is statically configured as a tree where the root node produces in output the detection map. This work represents a preliminary study through which we want to move from detection to segmentation and recognition of objects of interest. We have compared our model with other detection algorithms using a standard dataset and the results are encouraging. The results highlight the advantages and problems that will guide the evolution of the proposed model.