Attention and Feature Fusion SSD for Remote Sensing Object Detection

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.

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Adaptive Multi-Level Feature Fusion and Attention-Based Network for Arbitrary-Oriented Object Detection in Remote Sensing Imagery

Neurocomputing ◽

10.1016/j.neucom.2021.04.011 ◽

2021 ◽

Author(s):

Luchang Chen ◽

Chunsheng Liu ◽

Faliang Chang ◽

Shuang Li ◽

Zhaoying Nie

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion ◽

Remote Sensing Imagery ◽

Multi Level ◽

Oriented Object

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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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Real-Time Garbage Object Detection With Data Augmentation and Feature Fusion Using SUAV Low-Altitude Remote Sensing Images

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3074415 ◽

2021 ◽

pp. 1-5

Author(s):

Weiyang Chen ◽

Haifeng Wang ◽

Hao Li ◽

Quanjing Li ◽

Yang Yang ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Real Time ◽

Data Augmentation ◽

Feature Fusion ◽

Remote Sensing Images ◽

Low Altitude

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Object Detection for Remote Sensing Images based on Guided Anchoring and Feature Fusion

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324652 ◽

2020 ◽

Author(s):

Wei Wang ◽

Zhuangzhuang Tian ◽

Ronghui Zhan ◽

Jun Zhang ◽

Zhaowen Zhuang

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion ◽

Remote Sensing Images

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Efficient Object Detection Framework and Hardware Architecture for Remote Sensing Images

Remote Sensing ◽

10.3390/rs11202376 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2376 ◽

Cited By ~ 4

Author(s):

Li ◽

Zhang ◽

Wu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Computational Complexity ◽

Object Detection ◽

Graphics Processing Units ◽

Feature Fusion ◽

Hardware Architecture ◽

Single Shot ◽

Remote Sensing Images ◽

Feature Maps

Object detection in remote sensing images on a satellite or aircraft has important economic and military significance and is full of challenges. This task requires not only accurate and efficient algorithms, but also highperformance and low power hardware architecture. However, existing deep learning based object detection algorithms require further optimization in small objects detection, reduced computational complexity and parameter size. Meanwhile, the generalpurpose processor cannot achieve better power efficiency, and the previous design of deep learning processor has still potential for mining parallelism. To address these issues, we propose an efficient contextbased feature fusion single shot multibox detector (CBFFSSD) framework, using lightweight MobileNet as the backbone network to reduce parameters and computational complexity, adding feature fusion units and detecting feature maps to enhance the recognition of small objects and improve detection accuracy. Based on the analysis and optimization of the calculation of each layer in the algorithm, we propose efficient hardware architecture of deep learning processor with multiple neural processing units (NPUs) composed of 2D processing elements (PEs), which can simultaneously calculate multiple output feature maps. The parallel architecture, hierarchical onchip storage organization, and the local register are used to achieve parallel processing, sharing and reuse of data, and make the calculation of processor more efficient. Extensive experiments and comprehensive evaluations on the public NWPU VHR10 dataset and comparisons with some stateoftheart approaches demonstrate the effectiveness and superiority of the proposed framework. Moreover, for evaluating the performance of proposed hardware architecture, we implement it on Xilinx XC7Z100 field programmable gate array (FPGA) and test on the proposed CBFFSSD and VGG16 models. Experimental results show that our processor are more power efficient than general purpose central processing units (CPUs) and graphics processing units (GPUs), and have better performance density than other stateoftheart FPGAbased designs.

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Detection of Schools in Remote Sensing Images Based on Attention-Guided Dense Network

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10110736 ◽

2021 ◽

Vol 10 (11) ◽

pp. 736

Author(s):

Han Fu ◽

Xiangtao Fan ◽

Zhenzhen Yan ◽

Xiaoping Du

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion ◽

State Of The Art ◽

Feature Representation ◽

Detection Accuracy ◽

Dense Network ◽

Remote Sensing Images ◽

Composite Object ◽

Detection Algorithms

The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.

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A Lightweight Object Detection Framework for Remote Sensing Images

Remote Sensing ◽

10.3390/rs13040683 ◽

2021 ◽

Vol 13 (4) ◽

pp. 683

Author(s):

Lang Huyan ◽

Yunpeng Bai ◽

Ying Li ◽

Dongmei Jiang ◽

Yanning Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Real Time ◽

Large Scale ◽

Feature Fusion ◽

Computational Cost ◽

Feature Representation ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Low Level

Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.

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