Journal Article

Hybrid dilated multilayer faster RCNN for object detection

Xin, Fangfang, Pan, Hongguang, Zhang, Huipeng

2024

Overview

Faster region-based convolution neural network (Faster RCNN) architecture was proposed as an efficient object detection method, wherein a CNN is used to extract image features. However, CNNs require a large number of learning parameters, and an excessive amount of pooling layers lead to a loss of information on small objects, which may affect efficiency. In this study, we proposed a hybrid dilated multilayer Faster RCNN model to address this problem. The key contributions of this work are summarized as follows: (1) We substituted a hybrid dilated CNN (HDC) model for the VGG16 network used in the original Faster RCNN architecture to extract features and ensure portability. We also used a LeakyReLU activation function to improve the mapping ability of negative input information to detect objects rapidly and accurately. (2) We used a multilayer feature spatial pyramid to convert single-scale features into multi-scale features, and higher-resolution information was obtained through a deconvolutional network to achieve more accurate object detection. (3) We conducted experiments to verify the performance of the proposed HDMF-RCNN model using the Microsoft COCO data set. The results indicated that the accuracy of HDMF-RCNN was 8.12% greater than that of the traditional Faster RCNN model, and the training loss and training time were lower by 44.64% and 39.46% on average, respectively. Overall, the results verified that HDMF-RCNN can significantly improve on the efficiency of existing object detection methods. As an independent feature extraction network, HDC can be adapted to different network frameworks.

• Publisher: Springer Berlin Heidelberg
• Subject: Artificial Intelligence / Computer Graphics / Computer Science / Image Processing and Computer Vision / Original Article