scholarly journals Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3776
Author(s):  
Linlin Zhu ◽  
Xun Geng ◽  
Zheng Li ◽  
Chun Liu
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Visual Object ◽  
Art Object ◽  
Geological Processes ◽  
Landing Sites ◽  
New Feature

It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.

scholarly journals Object Detection Network Based on Feature Fusion and Attention Mechanism

2019 ◽  
Vol 11 (1) ◽  
pp. 9 ◽  
Author(s):  
Ying Zhang ◽  
Yimin Chen ◽  
Chen Huang ◽  
Mingke Gao
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Empirical Evaluation ◽  
Attention Mechanism ◽  
Detection Accuracy ◽  
Small Object ◽  
Art Object ◽  
Pascal Voc ◽  
Almost All ◽  
The Impact

In recent years, almost all of the current top-performing object detection networks use CNN (convolutional neural networks) features. State-of-the-art object detection networks depend on CNN features. In this work, we add feature fusion in the object detection network to obtain a better CNN feature, which incorporates well deep, but semantic, and shallow, but high-resolution, CNN features, thus improving the performance of a small object. Also, the attention mechanism was applied to our object detection network, AF R-CNN (attention mechanism and convolution feature fusion based object detection), to enhance the impact of significant features and weaken background interference. Our AF R-CNN is a single end to end network. We choose the pre-trained network, VGG-16, to extract CNN features. Our detection network is trained on the dataset, PASCAL VOC 2007 and 2012. Empirical evaluation of the PASCAL VOC 2007 dataset demonstrates the effectiveness and improvement of our approach. Our AF R-CNN achieves an object detection accuracy of 75.9% on PASCAL VOC 2007, six points higher than Faster R-CNN.

scholarly journals Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liming Zhou ◽  
Haoxin Yan ◽  
Chang Zheng ◽  
Xiaohan Rao ◽  
Yahui Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
High Rate ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Aircraft Detection ◽  
Low Rate

Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.

scholarly journals Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Manhuai Lu ◽  
Liqin Chen
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Specular Reflection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
External Disturbances ◽  
Feature Enhancement ◽  
Art Object ◽  
Testing Dataset ◽  
Pattern Information

The accuracy of object detection based on kitchen appliance scene images can suffer severely from external disturbances such as various levels of specular reflection, uneven lighting, and spurious lighting, as well as internal scene-related disturbances such as invalid edges and pattern information unrelated to the object of interest. The present study addresses these unique challenges by proposing an object detection method based on improved faster R-CNN algorithm. The improved method can identify object regions scattered in various areas of complex appliance scenes quickly and automatically. In this paper, we put forward a feature enhancement framework, named deeper region proposal network (D-RPN). In D-RPN, a feature enhancement module is designed to more effectively extract feature information of an object on kitchen appliance scene. Then, we reconstruct a U-shaped network structure using a series of feature enhancement modules. We have evaluated the proposed D-RPN on the dataset we created. It includes all kinds of kitchen appliance control panels captured in nature scene by image collector. In our experiments, the best-performing object detection method obtained a mean average precision mAP value of 89.84% in the testing dataset. The test results show that the proposed improved algorithm achieves higher detecting accuracy than state-of-the-art object detection methods. Finally, our proposed detection method can further be used in text recognition.

scholarly journals SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12661-12668 ◽  
Author(s):  
Lewei Yao ◽  
Hang Xu ◽  
Wei Zhang ◽  
Xiaodan Liang ◽  
Zhenguo Li
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Computational Cost ◽  
Search Space ◽  
Detection Methods ◽  
Structural Level ◽  
Neural Architecture ◽  
Art Object ◽  
Searching Strategy

The state-of-the-art object detection method is complicated with various modules such as backbone, RPN, feature fusion neck and RCNN head, where each module may have different designs and structures. How to leverage the computational cost and accuracy trade-off for the structural combination as well as the modular selection of multiple modules? Neural architecture search (NAS) has shown great potential in finding an optimal solution. Existing NAS works for object detection only focus on searching better design of a single module such as backbone or feature fusion neck, while neglecting the balance of the whole system. In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection. Specifically, Structural-level searching stage first aims to find an efficient combination of different modules; Modular-level searching stage then evolves each specific module and pushes the Pareto front forward to a faster task-specific network. We consider a multi-objective search where the search space covers many popular designs of detection methods. We directly search a detection backbone without pre-trained models or any proxy task by exploring a fast training from scratch strategy. The resulting architectures dominate state-of-the-art object detection systems in both inference time and accuracy and demonstrate the effectiveness on multiple detection datasets, e.g. halving the inference time with additional 1% mAP improvement compared to FPN and reaching 46% mAP with the similar inference time of MaskRCNN.

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.

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