AD RCNN Adaptive Dynamic Neural Network for Small Object Detection

With the large-scale commercialization of 5G networks, Internet of Things (IoT) applications keep on emerging in recent years. Real-time environmental awareness is an essential part of various IoT applications, e.g., self-driving vehicles. Object detection plays a fundamental role in real-time environmental awareness, which is responsible for acquiring valuable object information from the environment automatically. Despite of the fast progress for object detection in general, small object detection still faces challenges. Because of the restricted scales, small objects are only capable of generating relatively week features after multiple convolutional layers, thus causing low detection accuracy. Existing schemes mostly focus on extracting rich multiscale features, e.g., generating high-resolution features through generative adversarial networks (GANs), or generating multiscale features through feature combination. Nevertheless, these schemes require complex network implementation, and usually suffer from high processing delay because of high-resolution images. To resolve the problems mentioned above, we propose an adaptive dynamic neural network (AD-RCNN) that consists of three fundamental improvements. We first propose a dynamic region proposal network to improve the quality of region proposals. We then introduce a visual attention scheme to generate features of regions. Finally, we put forward an adaptive dynamic training module to optimize final detection results. Experimental results demonstrate that AD-RCNN outperforms the state-of-the-art from the perspectives of mAP and frames per second (FPS). Specifically, at the resolution of 1024 of TT100K data set, AD-RCNN achieves 68.8% mAP, which outperforms the baseline Faster RCNN by 8.52%.