One-stage object detection knowledge distillation via adversarial learning

2021 ◽  
Author(s):  
Na Dong ◽  
Yongqiang Zhang ◽  
Mingli Ding ◽  
Shibiao Xu ◽  
Yancheng Bai
Keyword(s):  
Object Detection ◽  
Adversarial Learning ◽  
One Stage ◽  
Knowledge Distillation

scholarly journals GAN-Knowledge Distillation for One-Stage Object Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 60719-60727
Author(s):  
Wanwei Wang ◽  
Wei Hong ◽  
Feng Wang ◽  
Jinke Yu
Keyword(s):  
Object Detection ◽  
One Stage ◽  
Knowledge Distillation

Revisiting knowledge distillation for light-weight visual object detection

2021 ◽  
Vol 43 (13) ◽  
pp. 2888-2898
Author(s):  
Tianze Gao ◽  
Yunfeng Gao ◽  
Yu Li ◽  
Peiyuan Qin
Keyword(s):  
Object Detection ◽  
Essential Element ◽  
Detection Algorithm ◽  
Positive Sample ◽  
Detection Methods ◽  
Visual Object ◽  
Light Weight ◽  
Model Compression ◽  
Novel Approach ◽  
Knowledge Distillation

An essential element for intelligent perception in mechatronic and robotic systems (M&RS) is the visual object detection algorithm. With the ever-increasing advance of artificial neural networks (ANN), researchers have proposed numerous ANN-based visual object detection methods that have proven to be effective. However, networks with cumbersome structures do not befit the real-time scenarios in M&RS, necessitating the techniques of model compression. In the paper, a novel approach to training light-weight visual object detection networks is developed by revisiting knowledge distillation. Traditional knowledge distillation methods are oriented towards image classification is not compatible with object detection. Therefore, a variant of knowledge distillation is developed and adapted to a state-of-the-art keypoint-based visual detection method. Two strategies named as positive sample retaining and early distribution softening are employed to yield a natural adaption. The mutual consistency between teacher model and student model is further promoted through a hint-based distillation. By extensive controlled experiments, the proposed method is testified to be effective in enhancing the light-weight network’s performance by a large margin.

Spatial likelihood voting with self-knowledge distillation for weakly supervised object detection

2021 ◽  
pp. 104314
Author(s):  
Ze Chen ◽  
Zhihang Fu ◽  
Jianqiang Huang ◽  
Mingyuan Tao ◽  
Rongxin Jiang ◽  
...  
Keyword(s):  
Object Detection ◽  
Knowledge Distillation ◽  
Self Knowledge ◽  
Weakly Supervised

scholarly journals Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3341 ◽  
Author(s):  
Hilal Tayara ◽  
Kil Chong
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Computation Time ◽  
Aerial Images ◽  
Feature Maps ◽  
Two Stage ◽  
One Stage ◽  
Wide Range ◽  
Feature Pyramid ◽  
Very High

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.

scholarly journals One-Stage Multi-Sensor Data Fusion Convolutional Neural Network for 3D Object Detection

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1434 ◽  
Author(s):  
Minle Li ◽  
Yihua Hu ◽  
Nanxiang Zhao ◽  
Qishu Qian
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Data Fusion ◽  
Object Detection ◽  
Sensor Data ◽  
3D Object ◽  
Sensor Data Fusion ◽  
One Stage ◽  
Multi Sensor Data Fusion ◽  
3D Object Detection

Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of various information advantages and improve the performance of object detection, we proposed a Complex-Retina network, a convolution neural network for 3D object detection based on multi-sensor data fusion. Firstly, a unified architecture with two feature extraction networks was designed, and the feature extraction of point clouds and images from different sensors realized synchronously. Then, we set a series of 3D anchors and projected them to the feature maps, which were cropped into 2D anchors with the same size and fused together. Finally, the object classification and 3D bounding box regression were carried out on the multipath of fully connected layers. The proposed network is a one-stage convolution neural network, which achieves the balance between the accuracy and speed of object detection. The experiments on KITTI datasets show that the proposed network is superior to the contrast algorithms in average precision (AP) and time consumption, which shows the effectiveness of the proposed network.

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