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Learning Point-Guided Localization for detection in Remote Sensing Images

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 1084-1094页

作者： Song, Qing Yang, Fan Yang, Lu Liu, Chun Hu, Mengjie Xia, Lurui Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Vis Lab Beijing 100876 Peoples R China Space Engn Univ Beijing 101416 Peoples R China

object detection in remote sensing images is challenging due to the dense distribution and arbitrary angle of the objects. It is a consensus that the oriented bounding box (OBB) is more suitable to represent the aerial objects. However, there are some extreme cases in regression-based OBB detection that make the regression target discontinuous, resulting in the poor performance. In this article, an analysis of the formats of OBB and the problems in its regression is presented, following with an exploration of transform localization from regression to keypoint estimation, which could be applied to avoid the problem of discontinuous regression target. Our novel method is called object-wise Point-guided Localization Detector (OPLD). Continuously, a new prediction of center-point is introduced to refine the results, as the truncation problem caused by the cut graph. Lastly, in order to figure the problem of inconsistency between the localization quality and the classification score, both the endpoint scores and the classification score are adopted weighting as a result score. Experimental results are based on two widely used datasets, i.e., DOTA and HRSC2016. OPLD achieve 76.43% mAP and 78.35% mAP in OBB and horizontal bounding boxes tasks of DOTA-v1.0, which achieves state-of-the-art performance, respectively. Project page at https://***/yf19970118/OPLD-Pytorch.

关键词： Remote sensing Detectors Proposals object detection Task analysis Estimation Pipelines Convolutional neural network deep learning oriented object detection remote sensing

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KeyShip: Towards High-Precision oriented SAR Ship detection Using Key Points

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REMOTE SENSING 2023年第8期15卷 2035-2035页

作者： Ge, Junyao Tang, Yiping Guo, Kaitai Zheng, Yang Hu, Haihong Liang, Jimin Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Synthetic Aperture Radar (SAR) is an all-weather sensing technology that has proven its effectiveness for ship detection. However, detecting ships accurately with oriented bounding boxes (OBB) on SAR images is challenging due to arbitrary ship orientations and misleading scattering. In this article, we propose a novel anchor-free key-point-based detection method, KeyShip, for detecting orientated SAR ships with high precision. Our approach uses a shape descriptor to model a ship as a combination of three types of key points located at the short-edge centers, long-edge centers, and the target center. These key points are detected separately and clustered based on predicted shape descriptors to construct the final OBB detection results. To address the boundary problem that arises with the shape descriptor representation, we propose a soft training target assignment strategy that facilitates successful shape descriptor training and implicitly learns the shape information of the targets. Our experimental results on three datasets (SSDD, RSDD, and HRSC2016) demonstrate our proposed method's high performance and robustness.

关键词： oriented object detection ship detection synthetic aperture radar (SAR) key point detection shape descriptor

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RiDOP: A Rotation-Invariant Detector with Simple oriented Proposals in Remote Sensing Images

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REMOTE SENSING 2023年第3期15卷 594-594页

作者： Wei, Chongyang Ni, Weiping Qin, Yao Wu, Junzheng Zhang, Han Liu, Qiang Cheng, Kenan Bian, Hui Northwest Inst Nucl Technol Xian 710024 Peoples R China Natl Univ Def Technol Coll Elect Sci Changsha 410073 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China

Compared with general object detection with horizontal bounding boxes in natural images, oriented object detection in remote sensing images is an active and challenging research topic as objects are usually displayed in arbitrary orientations. To model the variant orientations of oriented objects, general CNN-based methods usually adopt more parameters or well-designed modules, which are often complex and inefficient. To address this issue, the detector requires two key components to deal with: (i) generating oriented proposals in a light-weight network to achieve effective representation of arbitrarily oriented objects;(ii) extracting the rotation-invariant feature map in both spatial and orientation dimensions. In this paper, we propose a novel, lightweight rotated region proposal network to produce arbitrary-oriented proposals by sliding two vertexes only on adjacent sides and adopt a simple yet effective representation to describe oriented objects. This may decrease the complexity of modeling orientation information. Meanwhile, we adopt the rotation-equivariant backbone to generate the feature map with explicit orientation channel information and utilize the spatial and orientation modules to obtain completely rotation-invariant features in both dimensions. Without tricks, extensive experiments performed on three challenging datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016 demonstrate that our proposed method can reach state-of-the-art accuracy while reducing the model size by 40% in comparison with the previous best method.

关键词： oriented object detection object representation rotation-invariant feature image processing remote sensing

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Complete Rotated Localization Loss Based on Super-Gaussian Distribution for Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Li, Zhonghua Hou, Biao Wu, Zitong Guo, Zhengxi Ren, Bo Guo, Xianpeng Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China

Localization regression in oriented object detection tasks has long faced boundary discontinuity and angular discontinuity problems induced by periodic angles. These problems were successfully resolved by using a 2-D Gaussian distribution to modeling the oriented bounding box (OBB). However, the angular information of square-like objects will be lost when they are converted to 2-D Gaussian distribution, forming a systematic problem. Its fundamental reason is that when the aspect ratio of the object tends to 1, the equiprobability curve of 2-D Gaussian distribution degenerates from an ellipse to a circle, thus losing the orientation information of the rotated object. This results in the bounding boxes of such square-like objects not being learned effectively. To resolve this problem, we used the Lame curve (or superellipse) to modify the existing 2-D Gaussian function and designed a super-Gaussian distribution. This distribution can maintain anisotropy at arbitrary aspect ratios, thus preserving the angular information of the oriented object. We used the Kullback-Leibler (KL) divergence to measure the distance between two super-Gaussian distributions and convert it into a localization loss (SGKLD) by a function. SGKLD is an improved version of KLD loss. By modifying the form of the probability distribution, we elegantly fix the angle missing problem of the traditional Gaussian distribution. We validated the effectiveness of the proposed algorithm on several datasets and obtained the performance of state-of-the-art (SOTA). Our algorithm achieves a mean average precision (mAP) of 80.07, 76.59, 62.27, and 90.55/98.13 on the DOTA-v1.0, DOTA-v1.5, DOTA-v2.0, and HRSC2016 datasets, respectively.

关键词： Gaussian distribution Task analysis Location awareness Shape Probability distribution object detection Systematics Aerial object detection Kullback-Leibler (KL) divergence oriented object detection super-Gaussian distribution superellipse

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Learnable Loss Balancing in Anchor-Free oriented Detectors for Aerial object

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Wang, Kai Xiao, Zhifeng Wan, Qiao Tan, Xiaowei Li, Deren Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China

oriented object detection plays an important role in aerial image interpretation. Image processing speed is also essential due to massive amounts of aerial images. Anchor-free oriented detectors with fast processing speed are generally accepted despite the absence of preset anchors, contributing to their performance gap with anchor-based detectors. Most anchor-free oriented detectors are carefully designed by defining samples according to target characteristics, which require substantial prior knowledge, to realize improved performance. This study proposes an anchor-free oriented detector (termed rfpoint) that requires minimal prior knowledge. Moreover, this study mainly aims to introduce a dynamic sample definition strategy. This strategy is modeled as a dynamic regulating process, wherein the classification and box regression interact until the model converges. A rotating quality-driven loss (RQDL) and an adaptive-weight box loss (AWBL) are also proposed to realize the aforementioned process. RQDL redefines positive and negative attributes of samples according to the distribution of rotating intersection-over-unit (IoU) between predictions and ground truth. AWBL adjusts the importance degree of candidate samples in the box regression through classification scores. The proposed method is then tested on three mainstream aerial image datasets (DOTA, DIOR, and HRSC2016). Results reveal that the proposed method achieves the best performance compared with other oriented detectors, whose mean average precision (mAP) is 79.92%, 70.88%, and 90.67%. Moreover, the method maintains the inference speed advantage of anchor-free detectors. An effective sample definition method can bridge the performance gap of anchor-free oriented detectors without minimizing inference speed.

关键词： Detectors Training Feature extraction Task analysis object detection Head Adaptation models Aerial image anchor-free detector label assignment oriented object detection

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Ship detection in Low-Quality SAR Images via an Unsupervised Domain Adaption Method

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REMOTE SENSING 2023年第13期15卷 3326页

作者： Pu, Xinyang Jia, Hecheng Xin, Yu Wang, Feng Wang, Haipeng Fudan Univ Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Beijing Inst Remote Sensing Informat Beijing 100192 Peoples R China

Ship detection in low-quality Synthetic Aperture Radar (SAR) images poses a persistent challenge. Noise signals in complex environments disrupt imaging conditions, hindering SAR systems from acquiring precise target information, thereby significantly compromising the performance of detectors. Some methods mitigate interference via denoising techniques, while others introduce noise using classical methods to learn target features in the presence of noise. This conundrum is regarded as a cross-domain problem in this paper;a ship detection method in low-quality images is proposed to learn features of targets and shrink serious deterioration of detection performance by utilizing Generative Adversarial Networks (GANs). First, an image-to-image translation task is implemented using CycleGAN to generate low-quality SAR images with complex interference from the source domain to the target domain. Second, with the annotation inheritance, these generated SAR images participate in a training process to improve the detection accuracy and model robustness. Multiple experiments indicate that the proposed method conspicuously improves the detection performance and efficaciously reduces the missed detection rate in the SAR ship detection task. This cross-domain approach achieved outstanding improvements in the form of 11.0% mAP and 3.22% mAP on the GaoFen-3 ship dataset and SRSSD-V1.0, respectively. In addition, the characteristics and potentials of near-shore and off-shore SAR image reconstruction with style transfer based on Generative Adversarial Networks were explored and analyzed in this work.

关键词： deep learning domain adaption synthetic aperture radar oriented object detection Generative Adversarial Networks

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A computer vision-based approach for respiration rate monitoring of group housed pigs

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期210卷

作者： Wang, Meiqing Li, Xue Larsen, Mona L. V. Liu, Dong Rault, Jean-Loup Norton, Tomas Katholieke Univ Leuven KU LEUVEN Fac Biosci Engn Kasteelpark Arenberg 30 B-3001 Heverlee Belgium Swiss Fed Inst Technol Inst Agr Sci Anim Nutr Univ Str 2 CH-8092 Zurich Switzerland Jiangsu Acad Agr Sci Inst Agr Facil & Equipment Nanjing 210014 Peoples R China Aarhus Univ Dept Anim Sci Blichers Alle 20 DK-8830 Aarhus Denmark Univ Vet Med Vetmeduni Vienna Inst Anim Welf Sci ITT A-1210 Vienna Austria

In recent years, respiration rate (RR) monitoring using video data has been explored by researchers with rela-tively good success. However, the approaches used so far require the manual identification of the region of interest (ROI) in the image of the animal. When monitoring farm animals, typically housed in groups, such manual actions would entail an excessive time investment and increase the level of subjectivity in the application of the method. The aim of this study was to design a RR monitoring system targeted at group-housed animal applications. The developed system first selected video clips where pigs were in a resting status. Then an oriented object detector was used to detect each animal and select the ROI without manual intervention. Finally the RR is estimated by analyzing the time-varying features extracted from the ROI. Videos of group-housed pigs were collected to test the method, in which 5 pigs were included. Four pigs wore an ECG belt around the abdomen to collect the gold standard (GS) RR measures while a control pig did not wear a belt, with the GS for the control pig obtained through manually observation. The comparison between RR obtained by the computer vision (CV) method and the GS showed good agreement with a mean absolute error of 2.38 breath per minute (bpm) in the 4 pigs wearing belts and 1.72 bpm in the control pig, a root mean square error of 3.46 bpm in the 4 pigs wearing belts and 2.26 bpm in the control pig, and a correlation coefficient of 0.92 in the 4 pigs wearing belts and 0.95 in the control pig.

关键词： Physiological monitoring oriented object detection RGB video Breathing Animal welfare

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IndustrialEdgeML - End-to-end edge-based computer vision systemfor Industry 5.0

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Procedia Computer Science 2023年 217卷 594-603页

作者： Raphael Wagner Mario Matuschek Philipp Knaack Michael Zwick Manuela Geiß

State-of-the-art object detection methods gain increasing attention in various industrial applications. However, current vision systems typically rely on cloud services for data storage and training of AI models. In this work, we present an end-to-end vision system for bin-picking applications at the edge of industrial premises via industrial computers, where bin-picking refers to the dedicated selection and displacement of objects into designated areas. Our system is designed for human-robot collaboration in a range of distinct challenging industrial environments with a focus on automatically learning new objects. To this end, we propose a new approach for automated oriented object detection consisting of three stages: (i) automatic training data labeling for the new object based on autoencoders, (ii) object detection using the YOLOR-S architecture, and (iii) inference of the object's orientation, more precisely, rotated bounding box detection based on attention maps using the TS-CAM algorithm. Our method achieves state-of-the-art performance, without the need for annotations of rotated bounding boxes, when compared to the single-stage architecture S 2 A-Net for rotated bounding box detection. In particular, the system maintains high-quality performance not only for learning all objects at once but also for incrementally learning one object after another.

关键词： Attention Mechanisms Autonomous Production Incremental Learning oriented object detection

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A Novel object Detector Based on High-Quality Rotation Proposal Generation and Adaptive Angle Optimization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Qiao, Yajun Miao, Lingjuan Zhou, Zhiqiang Ming, Qi Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Currently, reliable and accurately oriented detection in remote sensing images still needs to be improved. The wide variation of object shapes and orientations in the remote sensing images usually leads to two issues in two-stage oriented object detectors. One issue is how to generate high-quality rotation proposals. The other is the angular error sensitivity to the aspect ratios in the angle optimization process. In this article, we propose a novel rotation proposal generation and optimization detector, which is based on high-quality rotation proposal generation and adaptive angle optimization to solve these two issues. The proposed method mainly establishes the geometric relationship guided region proposal networks (GRG-RPNs) and the adaptive angle optimization head (AAO-Head) to achieve more accurate oriented object detection. The GRG-RPN only uses a simple network and a small number of horizontal anchors to predict high-quality rotation proposals. This approach was derived via the calculation based on the theoretical analysis of the geometric relationship between the oriented bounding boxes (OBBs) and their external horizontal bounding boxes (EHBBs). The AAO-Head solves the angular error sensitivity to the aspect ratios and achieves adaptive angle optimization using a new regression parameter, which is defined based on the theoretical analysis of the relationship between the intersection over union (IoU), the angular errors, and the aspect ratios. The experiments show that our method can achieve a 2.5% mAP improvement average versus the compared state-of-the-art (SOTA) methods, and achieve 0.5% mAP improvement versus the next best method-oriented regions with CNN features (R-CNN) with fewer regression parameters and a simpler regression approach.

关键词： Adaptive angle optimization head (AAO-Head) geometric relationship guided region proposal network (GRG-RPN) oriented object detection

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Voltage-Induced Heating Defect detection for Electrical Equipment in Thermal Images

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ENERGIES 2023年第24期16卷 8036-8036页

作者： Lin, Ying Li, Zhuangzhuang Sun, Yiwei Yang, Yi Zheng, Wenjie State Grid Shandong Elect Power Res Inst Jinan 250002 Peoples R China

Voltage-induced heating defect is a type of defect that may occur in transformation substation equipment. Although this type of defect is less common compared to current-induced heating defects, it is crucial to identify it due to its association with severe insulation degradation problems that require prompt intervention. However, the temperature variations caused by these defects may be relatively subtle, making it challenging to distinguish them in thermal images. In this work, considering the characteristics of voltage-induced heating defects and the scarcity of defect data, we propose a two-stage method for defect detection. In the first stage, we employ oriented R-CNN to detect oriented parts of the equipment, accurately localizing the centerline of each part. In the second stage, we extract the temperature distribution along the centerline of specific parts and discretize them as features. Subsequently, we train one-class support vector machines based on the features extracted from normal images for defect diagnosis. Experimental results demonstrate that the proposed method is capable of accurately detecting defects while maintaining a low false positive rate.

关键词： electrical equipment defect detection voltage-induced heating defect thermal image analysis oriented object detection

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