FA-YOLO: An Improved YOLO Model for Infrared Occlusion Object Detection under Confusing Background

Infrared target detection is a popular applied field in object detection as well as a challenge. This paper proposes the focus and attention mechanism-based YOLO (FA-YOLO), which is an improved method to detect the infrared occluded vehicles in the complex background of remote sensing images. Firstly, we use GAN to create infrared images from the visible datasets to make sufficient datasets for training as well as using transfer learning. Then, to mitigate the impact of the useless and complex background information, we propose the negative sample focusing mechanism to focus on the confusing negative sample training to depress the false positives and increase the detection precision. Finally, to enhance the features of the infrared small targets, we add the dilated convolutional block attention module (dilated CBAM) to the CSPdarknet53 in the YOLOv4 backbone. To verify the superiority of our model, we carefully select 318 infrared occluded vehicle images from the VIVID-infrared dataset for testing. The detection accuracy-mAP improves from 79.24% to 92.95%, and the F1 score improves from 77.92% to 88.13%, which demonstrates a significant improvement in infrared small occluded vehicle detection.

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Small Targets Detection for Transmission Tower Based on SRGAN and Faster RCNN

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666211026143543 ◽

2021 ◽

Vol 14 ◽

Author(s):

Runze Liu ◽

Guangwei Yan ◽

Hui He ◽

Yubin An ◽

Ting Wang ◽

...

Keyword(s):

Object Detection ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Stable Operation ◽

Detection Accuracy ◽

Small Object ◽

Detection Model ◽

Tower Equipment ◽

Small Object Detection ◽

Small Targets

Background: Power line inspection is essential to ensure the safe and stable operation of the power system. Object detection for tower equipment can significantly improve inspection efficiency. However, due to the low resolution of small targets and limited features, the detection accuracy of small targets is not easy to improve. Objective: This study aimed to improve the tiny targets’ resolution while making the small target's texture and detailed features more prominent to be perceived by the detection model. Methods: In this paper, we propose an algorithm that employs generative adversarial networks to improve small objects' detection accuracy. First, the original image is converted into a super-resolution one by a super-resolution reconstruction network (SRGAN). Then the object detection framework Faster RCNN is utilized to detect objects on the super-resolution images. Result: The experimental results on two small object recognition datasets show that the model proposed in this paper has good robustness. It can especially detect the targets missed by Faster RCNN, which indicates that SRGAN can effectively enhance the detailed information of small targets by improving the resolution. Conclusion: We found that higher resolution data is conducive to obtaining more detailed information of small targets, which can help the detection algorithm achieve higher accuracy. The small object detection model based on the generative adversarial network proposed in this paper is feasible and more efficient. Compared with Faster RCNN, this model has better performance on small object detection.

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Object Detection Based on Global-Local Saliency Constraint in Aerial Images

Remote Sensing ◽

10.3390/rs12091435 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1435 ◽

Cited By ~ 1

Author(s):

Chengyuan Li ◽

Bin Luo ◽

Hailong Hong ◽

Xin Su ◽

Yajun Wang ◽

...

Keyword(s):

Object Detection ◽

Semantic Information ◽

Feature Fusion ◽

Aerial Images ◽

Natural Image ◽

Optical Remote Sensing ◽

Complex Background ◽

Bounding Boxes ◽

The Impact ◽

Oriented Bounding Boxes

Different from object detection in natural image, optical remote sensing object detection is a challenging task, due to the diverse meteorological conditions, complex background, varied orientations, scale variations, etc. In this paper, to address this issue, we propose a novel object detection network (the global-local saliency constraint network, GLS-Net) that can make full use of the global semantic information and achieve more accurate oriented bounding boxes. More precisely, to improve the quality of the region proposals and bounding boxes, we first propose a saliency pyramid which combines a saliency algorithm with a feature pyramid network, to reduce the impact of complex background. Based on the saliency pyramid, we then propose a global attention module branch to enhance the semantic connection between the target and the global scenario. A fast feature fusion strategy is also used to combine the local object information based on the saliency pyramid with the global semantic information optimized by the attention mechanism. Finally, we use an angle-sensitive intersection over union (IoU) method to obtain a more accurate five-parameter representation of the oriented bounding boxes. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed GLS-Net achieves a state-of-the-art detection performance.

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IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs11030286 ◽

2019 ◽

Vol 11 (3) ◽

pp. 286 ◽

Cited By ~ 24

Author(s):

Jiangqiao Yan ◽

Hongqi Wang ◽

Menglong Yan ◽

Wenhui Diao ◽

Xian Sun ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Object Detection ◽

Convolutional Neural Network ◽

State Of The Art ◽

Ground Truth ◽

Detection Performance ◽

Candidate Region ◽

Detection Accuracy ◽

Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

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Object Detection Network Based on Feature Fusion and Attention Mechanism

Future Internet ◽

10.3390/fi11010009 ◽

2019 ◽

Vol 11 (1) ◽

pp. 9 ◽

Cited By ~ 6

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.

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Subtask Attention Based Object Detection in Remote Sensing Images

Remote Sensing ◽

10.3390/rs13101925 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1925

Author(s):

Shengzhou Xiong ◽

Yihua Tan ◽

Yansheng Li ◽

Cai Wen ◽

Pei Yan

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion ◽

Detection Task ◽

Feature Representation ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Attention Network ◽

Multi Scale ◽

Automatic Interpretation

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.

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Elongated Small Object Detection from Remote Sensing Images Using Hierarchical Scale-Sensitive Networks

Remote Sensing ◽

10.3390/rs13163182 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3182

Author(s):

Zheng He ◽

Li Huang ◽

Weijiang Zeng ◽

Xining Zhang ◽

Yongxin Jiang ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Large Scale ◽

Small Scale ◽

Detection Accuracy ◽

Small Object ◽

Direction Vector ◽

Remote Sensing Images ◽

Ship Detection ◽

Hierarchical Scale

The detection of elongated objects, such as ships, from satellite images has very important application prospects in marine transportation, shipping management, and many other scenarios. At present, the research of general object detection using neural networks has made significant progress. However, in the context of ship detection from remote sensing images, due to the elongated shape of ship structure and the wide variety of ship size, the detection accuracy is often unsatisfactory. In particular, the detection accuracy of small-scale ships is much lower than that of the large-scale ones. To this end, in this paper, we propose a hierarchical scale sensitive CenterNet (HSSCenterNet) for ship detection from remote sensing images. HSSCenterNet adopts a multi-task learning strategy. First, it presents a dual-direction vector to represent the posture or direction of the tilted bounding box, and employs a two-layer network to predict the dual direction vector, which improves the detection block of CenterNet, and cultivates the ability of detecting targets with tilted posture. Second, it divides the full-scale detection task into three parallel sub-tasks for large-scale, medium-scale, and small-scale ship detection, respectively, and obtains the final results with non-maximum suppression. Experimental results show that, HSSCenterNet achieves a significant improved performance in detecting small-scale ship targets while maintaining a high performance at medium and large scales.

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RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images

Remote Sensing ◽

10.3390/rs12030389 ◽

2020 ◽

Vol 12 (3) ◽

pp. 389 ◽

Cited By ~ 6

Author(s):

Yangyang Li ◽

Qin Huang ◽

Xuan Pei ◽

Licheng Jiao ◽

Ronghua Shang

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Small Object ◽

Remote Sensing Images ◽

Feature Maps ◽

Attention Network ◽

Complex Background ◽

Bounding Box ◽

Feature Pyramid ◽

Public Datasets

Object detection has made significant progress in many real-world scenes. Despite this remarkable progress, the common use case of detection in remote sensing images remains challenging even for leading object detectors, due to the complex background, objects with arbitrary orientation, and large difference in scale of objects. In this paper, we propose a novel rotation detector for remote sensing images, mainly inspired by Mask R-CNN, namely RADet. RADet can obtain the rotation bounding box of objects with shape mask predicted by the mask branch, which is a novel, simple and effective way to get the rotation bounding box of objects. Specifically, a refine feature pyramid network is devised with an improved building block constructing top-down feature maps, to solve the problem of large difference in scales. Meanwhile, the position attention network and the channel attention network are jointly explored by modeling the spatial position dependence between global pixels and highlighting the object feature, for detecting small object surrounded by complex background. Extensive experiments on two remote sensing public datasets, DOTA and NWPUVHR -10, show our method to outperform existing leading object detectors in remote sensing field.

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Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

Electronics ◽

10.3390/electronics8101151 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1151 ◽

Cited By ~ 4

Author(s):

Xia Hua ◽

Xinqing Wang ◽

Ting Rui ◽

Dong Wang ◽

Faming Shao

Keyword(s):

Remote Sensing ◽

Visual Perception ◽

Object Detection ◽

Real Time ◽

Detection Accuracy ◽

Small Object ◽

Remote Sensing Images ◽

Feature Maps ◽

Convolutional Network ◽

Fully Convolutional Network

Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote sensing images, a cascaded convolutional neural network real-time object-detection framework for remote sensing images is proposed, which integrates visual perception and convolutional memory network reasoning. The detection framework is composed of two fully convolutional networks, namely, the strengthened object self-attention pre-screening fully convolutional network (SOSA-FCN) and the object accurate detection fully convolutional network (AD-FCN). SOSA-FCN introduces a self-attention module to extract attention feature maps and constructs a depth feature pyramid to optimize the attention feature maps by combining convolutional long-term and short-term memory networks. It guides the acquisition of potential sub-regions of the object in the scene, reduces the computational complexity, and enhances the network’s ability to extract multi-scale object features. It adapts to the complex background and small object characteristics of a large-scene remote sensing image. In AD-FCN, the object mask and object orientation estimation layer are designed to achieve fine positioning of candidate frames. The performance of the proposed algorithm is compared with that of other advanced methods on NWPU_VHR-10, DOTA, UCAS-AOD, and other open datasets. The experimental results show that the proposed algorithm significantly improves the efficiency of object detection while ensuring detection accuracy and has high adaptability. It has extensive engineering application prospects.

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Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images

Applied Sciences ◽

10.3390/app9061130 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1130 ◽

Cited By ~ 4

Author(s):

Eric Wang ◽

Yueping Li ◽

Zhe Nie ◽

Juntao Yu ◽

Zuodong Liang ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Object Detection ◽

Fine Tuning ◽

Detection Accuracy ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Feature Extraction Method ◽

Feature Based ◽

Fusion Feature

With the rapid growth of high-resolution remote sensing image-based applications, one of the fundamental problems in managing the increasing number of remote sensing images is automatic object detection. In this paper, we present a fusion feature-based deep learning approach to detect objects in high-resolution remote sensing images. It employs fine-tuning from ImageNet as a pre-training model to address the challenge of it lacking a large amount of training datasets in remote sensing. Besides, we improve the binarized normed gradients algorithm by multiple weak feature scoring models for candidate window selection and design a deep fusion feature extraction method with the context feature and object feature. Experiments are performed on different sizes of high-resolution optical remote sensing images. The results show that our model is better than regular models, and the average detection accuracy is 8.86% higher than objNet.

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An Approach on Image Processing of Deep Learning Based on Improved SSD

Symmetry ◽

10.3390/sym13030495 ◽

2021 ◽

Vol 13 (3) ◽

pp. 495

Author(s):

Liang Jin ◽

Guodong Liu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Remote Sensing Image ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Image Detection ◽

Ship Detection ◽

Real Time Detection

Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.

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