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1. AD-RCNN: Adaptive Dynamic Neural
Network for Small Object Detection
Abstract
With the large-scale commercialization of 5G networks, Internet of Things
(IoT) applications keep on emerging in recent years. Real
awareness is an essential part of various IoT applications, e.g., self
vehicles. Object detection
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 fa
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
adversarial networks (GANs), or generating multiscale features through
feature combination. Nevertheless, these schemes require complex network
implementation, and usually suffer from hig
RCNN: Adaptive Dynamic Neural
Network for Small Object Detection
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
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
atures, 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
RCNN: Adaptive Dynamic Neural
Network for Small Object Detection
scale commercialization of 5G networks, Internet of Things
time environmental
awareness is an essential part of various IoT applications, e.g., self-driving
time environmental
awareness, which is responsible for acquiring valuable object information from
the environment automatically. Despite of the fast progress for object
ces 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
resolution features through generative
adversarial networks (GANs), or generating multiscale features through
feature combination. Nevertheless, these schemes require complex network
h processing delay because of

2. 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%.