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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Zhang, Shuyang Wei, Yuntao Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9783031442124;9783031442131

Rotated object detection aims to identify and locate objects in images with oriented angles. In this case, the detection scenario varies a lot in multi-dimensions. Besides, multiple oriented objects always exist within a single image, which increase the complexity for angle prediction. Although representing the angle by an extra regression branch can enhance precision in rotated box detector, it's more plausible to optimize the prediction process in a global view including feature-extraction, detection field partition and angle regression branch. In this paper, we introduced a partitioned detection architecture to identify objects which are at different scales, corresponding to different feature levels of the network. Inspired by Vision Transformer, a series of novel attention layers are embedded seamlessly for extracting necessary features from different scales and regions. We also conducted a short-term diffusion process to produce Gaussian noise in original image considering generalizability. As is shown, our detectors show a significant improvement of mAP and balance between performance and accuracy in two challenging aerial detection task, DOTA and HRSC2016.

关键词： oriented object detection Cross-layer Attention Decoupled Head

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Metrological Characterization of a Clip Fastener assembly fault detection system based on Deep Learning

Metrological Characterization of a Clip Fastener assembly fa...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Rising Above Covid-19

作者： Gallo, Vincenzo Shallari, Irida Carratu, Marco O'Nils, Mattias Univ Salerno Dept Ind Engn Fisciano Italy Mid Sweden Univ Dept Elect Design Sundsvall Sweden

ISBN: (纸本)9781665453837

In a time when Artificial Intelligence (AI) technologies are nearly ubiquitous, railway construction and maintenance systems have not fully grasped the capabilities of such technologies. Traditional railway inspection methods rely on inspection from experienced workers, making such tasks costly from both, the monetary and the time perspective. From an overview of the state-of-the-art research in this area regarding AI-based systems, we observed that their main focus was solely on detection accuracy of different railway components. However, if we consider the critical importance of railway fastening in the overall safety of the railway, there is a need for a thorough analysis of these AI-based methodologies, to define their uncertainty also from a metrological perspective. In this article we address this issue, proposing an image-based system that detects the rotational displacement of the fastened railway clips. Furthermore, we provide an uncertainty analysis of the measurement system, where the resulting uncertainty is of 0.42 degrees, within the 3 degrees error margin defined by the clip manufacturer.

关键词： oriented object detection railway fastener clip angle measurement

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A Mask Guided oriented object Detector Based on Rotated Size-Adaptive Tricube Kernel

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Pan, Yushan Xu, Yang Wu, Zebin Wei, Zhihui Plaza, Javier Plaza, Antonio Nanjing Univ Sci & Technol NJUST Sch Comp Sci & Engn Nanjing 210094 Peoples R China Geol Explorat Technol Inst Jiangsu Prov Nanjing 210018 Peoples R China Jiangsu Prov Engn Res Ctr Airborne Detecting & Int Nanjing 210049 Peoples R China Univ Extremadura Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10003 Spain

oriented object detection is an important research topic in remote sensing. The detection of oriented objects in remote sensing images remains a daunting challenge due to their complex backgrounds, various sizes, diverse aspect ratios, and especially arbitrary orientations. In recent years, keypoint-based anchor-free object detectors have demonstrated outstanding performance in this field. However, in current anchor-free detectors, object keypoints are primarily generated using the Gaussian kernel function, which assumes a circular form. This representation falls short in accurately conveying an object's size and orientation. To address the aforementioned issue, this article proposes a keypoint-based oriented object detector called MRSDet, which innovatively adopts the Tricube kernel, scales, and rotates it, to better generate the center keypoint heatmap of the object. Besides, to improve the model's detection performance on oriented objects and improve its ability to perceive object keypoints and boundary boxes, we also design a large receptive field mask (LRFM) module, which is based on large convolution kernel decomposition and semantic segmentation masks. Taking the box boundary-aware vectors (BBAVectors) method as a baseline, we conduct experiments on multiple types of remote sensing datasets such as HRSC2016, UCAS-AOD, and SSDD+ datasets to verify the effectiveness and generalizability of the proposed method.

关键词： Convolution kernel decomposition keypoint-based detector oriented object detection remote sensing imagery Tricube kernel

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Leveraging Permuted Image Restoration for Improved Interpretation of Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Bai, Awen Chen, Jie Yang, Wei Men, Zhirong Zhang, Shengming Zeng, Hongcheng Xu, Weichen Cao, Jian Sch Elect & Informat Engn Beihang Univ Beijing 100191 Peoples R China Peking Univ Sch Software & Microelect Beijing 100871 Peoples R China

In this study, we introduce a novel self-supervised learning adapter based on permutated image restoration (PIR) for effectively transferring pretrained weights from natural images to remote sensing object detection tasks. The adapter's unique methodology encompasses a three-phase process: segmenting and permuting image blocks, estimating permutation matrices for sequence reconstruction, and applying specialized loss functions for accurate block positioning. The use of our approach results in the maintenance of fidelity in both absolute and relative block positions as demonstrated by the evaluation of block similarities. The empirical results indicate significant performance enhancements for diverse datasets spanning optical and synthetic aperture radar data types, including high resolution ship collections 2016 (HRSC2016), Small object detection dAtasets - Aerial (SODA-A), and rotated ship detection dataset (RSDD) while effectively avoiding overfitting.

关键词： oriented object detection permutated image restoration (PIR) permutation matrix estimation pretrained weights remote sensing self-supervised learning

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A rapid oriented detection method of virtual components for augmented assembly

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Du, Baigang Guo, Jingwei Guo, Jun Wang, Lei Li, Xixing Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China Hubei Univ Technol Sch Mech Engn Wuhan 430068 Peoples R China

Augmented assembly provides intuitive assembly guidelines to the assembly operators. With assembly tasks developing toward diversification, the position of virtual components that are superimposed on the real environment may change frequently and virtual components have different length-diameter ratios and overlap each other. However, existing object detection methods are time consuming and hardly separate virtual components in dense for augmented assembly. Therefore, this paper proposes a rapid oriented detection method based on swift oriented detection region -based convolutional network (SODR-CNN) for virtual components in augmented assembly. Specifically, a dual channel selection (DCS) structure is designed to extract spatial features by reducing redundant computation and memory access. Further, the novel backbone network, rapid network (RapidNet), is proposed building upon the DCS, which can decrease the inference latency of SODR-CNN. Finally, the comprehensive experiments demonstrate that the RapidNet achieves lower inference latency than comparison backbone networks, which means that the SODR-CNN achieves superior inference latency and detection accuracy. Moreover, intuitive evaluation experiments are carried out, in which the effectiveness of the proposed method is further verified by searching for several virtual components in a real environment.

关键词： Augmented assembly oriented object detection SODR-CNN RapidNet Dual channel selection structure

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Prototypical contrastive learning based oriented detector for kitchen waste

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NEUROCOMPUTING 2024年 601卷

作者： Ouyang, Lihan Fang, Leyuan Tang, Qi Ding, Shuaiyu Yu, Junwu Lin, Jiaxing Tang, Lin Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China Hunan Univ Coll Environm Sci & Engn Changsha Peoples R China

Automatic detection of kitchen waste enables the identification and quantification of non-degradable materials, such as plastics, metals, and other substances that cannot be easily decomposed. This approach increases the efficiency of the waste disposal process with significant time and cost savings compared to manual sorting. However, detecting non-degradable waste automatically presents a great challenge due to the deformability and directional uncertainty of the objects in kitchen waste images. object deformation leads to a lack of structural features, making it more difficult to distinguish objects. Direction uncertainty leads to redundant backgrounds when detecting with the horizontal box detector. To address these issues, we propose a kitchen waste detector (KWDet) for the automatic detection of non-degradable waste in kitchen waste. First, the KWDet introduces the oriented bounding box representation and rotated region of interest (RRoI) learner, which enables the oriented detection of non-degradable waste in kitchen waste. Second, the KWDet constructs a prototypical contrastive learning (PCL) based detection head to improve the classification performance by learning discriminative features of different non-degradable waste in kitchen waste. In the PCL based detection head, the prototype-sample contrastive loss is proposed to pull samples and prototypes from the same classes together while pushing samples and prototypes from the different classes apart. After that, we design the inter- prototype contrastive loss to learn the discriminative prototypes of different non-degradable waste. Finally, to evaluate the proposed KWDet, we construct a large kitchen waste detection dataset (KWDD), containing 13,873 kitchen waste images and 213,565 instances collected from Zhengzhou China with 5 classes. To validate the effectiveness of our proposed method, we performed ablation experiments and comparison experiments on the KWDD dataset. The experiments demonstrate that KW

关键词： Kitchen-waste detector Prototypical contrastive learning oriented object detection Convolutional neural network

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orientedFormer: An End-to-End Transformer-Based oriented object Detector in Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhao, Jiaqi Ding, Zeyu Zhou, Yong Zhu, Hancheng Du, Wen-Liang Yao, Rui El Saddik, Abdulmotaleb China Univ Min & Technol Mine Digitizat Engn Res Ctr Sch Comp Sci & Technol Minist Educ Xuzhou 221116 Peoples R China China Univ Min & Technol Innovat Res Ctr Disaster Intelligent Prevent & Eme Xuzhou 221116 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

oriented object detection in remote sensing images is a challenging task due to objects being distributed in multiorientation. Recently, end-to-end transformer-based methods have achieved success by eliminating the need for post-processing operators compared to traditional convolutional neural network (CNN)-based methods. However, directly extending transformers to oriented object detection presents three main issues: 1) objects rotate arbitrarily, necessitating the encoding of angles along with position and size;2) the geometric relations of oriented objects are lacking in self-attention, due to the absence of interaction between content and positional queries;and 3) oriented objects cause misalignment, mainly between values and positional queries in cross-attention, making accurate classification and localization difficult. In this article, we propose an end-to-end transformer-based oriented object detector, consisting of three dedicated modules to address these issues. First, Gaussian positional encoding (PE) is proposed to encode the angle, position, and size of oriented boxes using Gaussian distributions. Second, Wasserstein self-attention is proposed to introduce geometric relations and facilitate interaction between content and positional queries by utilizing Gaussian Wasserstein distance scores. Third, oriented cross-attention is proposed to align values and positional queries by rotating sampling points around the positional query according to their angles. Experiments on six datasets DIOR-R, a series of DOTA, HRSC2016, and ICDAR2015 show the effectiveness of our approach. Compared with previous end-to-end detectors, the orientedFormer gains 1.16 and 1.21 AP50 on DIOR-R and DOTA-v1.0, respectively, while reducing training epochs from 3 X to 1 X . The code is available at https://***/wokaikaixinxin/orientedFormer.

关键词： End-to-end detectors oriented object detection positional encoding (PE) remote sensing transformer End-to-end detectors oriented object detection positional encoding (PE) remote sensing transformer

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USING DEEP LEARNING TO IMPROVE detection AND DECODING OF BARCODES 29

USING DEEP LEARNING TO IMPROVE DETECTION AND DECODING OF BAR...

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IEEE International Conference on Image Processing (ICIP)

作者： Wang, Chaoxin Guevara, Nicolais Caragea, Doina Kansas State Univ Dept Comp Sci Manhattan KS 66506 USA Siena Anal 1256 W Cent St Franklin MA 02038 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

We propose an end-to-end pipeline for transforming raw images containing barcodes into sharp barcode images that can be accurately decoded. Our pipeline leverages recent deep learning approaches and consists of a rotation-decoupled detector (RDD) for oriented barcode detection and a deblurring model. The deblurring model uses a generative adversarial network (specifically, DeblurGAN-v2) trained on pairs of noisy and sharp images generated using ground truth numeric codes. Evaluation of the proposed pipeline using real barcode images enhanced by the DeblurGAN-v2 model shows a 14% improvement of the decoding rate as compared to the decoding rate obtained on the original images.

关键词： barcode decoding oriented object detection rotation angle barcode image deblurring

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Gradient Calibration Loss for Fast and Accurate oriented Bounding Box Regression

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Ming, Qi Miao, Lingjuan Zhou, Zhiqiang Song, Junjie Pizurica, Aleksandra Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Univ Ghent Fac Engn & Architecture B-9000 Ghent Belgium

oriented object detection has a very wide range of application scenarios. In recent years, a lot of rotation detectors have been designed to achieve high-performance oriented object detection. Intersection-over-union (IoU) is the commonly used indicator to evaluate the accuracy of detection performance. Many methods introduce IoU into the bounding box regression loss to achieve the aligned training and evaluation process for better performance. However, in this article, we demonstrate several drawbacks of rotated IoU loss through both experiments and theoretical derivation: 1) there is a negative correlation between the loss gradient and the angular error and 2) the optimization process of rotated IoU loss suffers from scale sensitivity, which is not conducive to model convergence. To solve the problems, we propose a gradient calibration loss (GCL) that optimizes the rotated IoU loss via gradient analysis and correction. We construct the optimized gradient in GCL to avoid IoU loss oscillation and scale sensitivity, thereby accelerating model convergence. Models supervised by GCL have a more stable training process, faster convergence, and better performance. Moreover, GCL can be easily introduced into the existing rotation detectors to achieve performance gains without extra inference overhead. Extensive experiments on multiple oriented object detection datasets and models demonstrate the superiority of our method. Our method achieves state-of-the-art performance on the mainstream benchmark datasets. The source code and models are available at https://***/ming71/GCL.

关键词： Convolutional neural network gradient analysis loss function oriented object detection

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Mask OBB: A Semantic Attention-Based Mask oriented Bounding Box Representation for Multi-Category object detection in Aerial Images

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REMOTE SENSING 2019年第24期11卷 2930-2930页

作者： Wang, Jinwang Ding, Jian Guo, Haowen Cheng, Wensheng Pan, Ting Yang, Wen Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ State Key Lab LIESMARS Wuhan 430072 Peoples R China

object detection in aerial images is a fundamental yet challenging task in remote sensing field. As most objects in aerial images are in arbitrary orientations, oriented bounding boxes (OBBs) have a great superiority compared with traditional horizontal bounding boxes (HBBs). However, the regression-based OBB detection methods always suffer from ambiguity in the definition of learning targets, which will decrease the detection accuracy. In this paper, we provide a comprehensive analysis of OBB representations and cast the OBB regression as a pixel-level classification problem, which can largely eliminate the ambiguity. The predicted masks are subsequently used to generate OBBs. To handle huge scale changes of objects in aerial images, an Inception Lateral Connection Network (ILCN) is utilized to enhance the Feature Pyramid Network (FPN). Furthermore, a Semantic Attention Network (SAN) is adopted to provide the semantic feature, which can help distinguish the object of interest from the cluttered background effectively. Empirical studies show that the entire method is simple yet efficient. Experimental results on two widely used datasets, i.e., DOTA and HRSC2016, demonstrate that the proposed method outperforms state-of-the-art methods.

关键词： oriented object detection aerial images convolutional neural networks

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