Overview of ThunderNet: Two-Stage Object Detection Model
ThunderNet is a state-of-the-art two-stage object detection model for detecting objects in images. The model is designed to address the computationally expensive structures of current two-stage detectors. Its backbone utilizes SNet, a ShuffleNetV2 inspired network that is designed for object detection. ThunderNet's detection head design is modeled after Light-Head R-CNN, with further compression of the Region Proposal Network (RPN) and R-CNN subnet.
The Design of ThunderNet
ThunderNet is designed to make object detection more efficient and cost-effective. The model uses two new architecture blocks, Context Enhancement Module (CEM) and Spatial Attention Module (SAM), to eliminate performance degradation caused by small backbones and small feature maps.
The Backbone
The backbone of ThunderNet is built on SNet, a deep convolutional neural network designed for object detection. SNet is inspired by ShuffleNetV2 and optimized for computing efficiency. It uses a series of shuffle operations to reduce computations and memory usage while maintaining the accuracy of the detection results. With its lightweight design, SNet can run smoothly on mobile devices and other low-power devices.
The Detection Head
The detection head of ThunderNet follows the design of Light-Head R-CNN, a state-of-the-art method for object detection. The detection head includes an RPN and an R-CNN subnet, which are further compressed to reduce the computation costs. With this design, ThunderNet achieves high accuracy in object detection while remaining computationally efficient.
The Architecture Blocks
ThunderNet uses two new architecture blocks, Context Enhancement Module (CEM) and Spatial Attention Module (SAM), to further improve detection accuracy and computational efficiency.
The Context Enhancement Module (CEM) is designed to leverage local and global context information by combining the feature maps from multiple scales. CEM enhances the representation of the features by capturing contextual information from various scales in the feature maps.
The Spatial Attention Module (SAM) is designed to refine the feature distribution in RoI warping. The module uses the information learned in RPN to refine the feature distribution and reduce the noise and redundancy introduced in RoI pooling. By doing so, the module improves the localization accuracy in object detection.
The Benefits of ThunderNet
ThunderNet achieves high detection accuracy with lower computation costs than other state-of-the-art two-stage detectors. It is optimized for mobile devices and other low-power devices, making it an efficient and cost-effective solution for object detection in various applications.
ThunderNet's design demonstrates the potential for efficient and accurate object detection models that can perform well on low-power devices, as well as more powerful computing systems. With the continued development of more efficient object detection models, we can expect to see more applications of object detection technology in various fields, such as autonomous vehicles, robotics, and surveillance systems.
