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共 209 条记录，以下是41-50 订阅

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Obstacle detection in Off-road Environments Based on LiDAR 4

Obstacle Detection in Off-road Environments Based on LiDAR

引用

4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Ahat, Arzigul Firkat, Eksan Mijit, Tayir Hamdulla, Askar Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350324303

Mapping off-road terrain is a challenging task, especially when compared to urban terrain. The complexity and roughness of off-road terrain make it difficult to achieve accurate mapping results. Obstacle detection in such environments is also a challenging task, as the performance of LiDAR-based methods depends on the point cloud density at the location of the obstacle. High line beam LiDAR is expensive, while low line beam LiDAR is sparse, making it difficult to obtain dense point clouds. In this paper, we propose a multi-LiDAR fusion approach that uses roof LiDAR as the primary sensor and front LiDAR as a supplement. The approach employs point cloud filtering, dedistortion, and point cloud registration to fuse data from multiple sensors. This results in a richer view of the environment and more intensive measurement data. We combine the voxel map with a twodimensional grid map and accelerate the voxel map using a GPU to generate accurate maps. Experimental results show that our proposed method achieves the highest accuracy and recall rates, improving the accuracy and robustness of obstacle detection in off-road environments. © 2023 IEEE.

关键词： Optical radar

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COT2DNet: Contextual 2D Attention for Scene Text Recognition 2

COT2DNet: Contextual 2D Attention for Scene Text Recognition

引用

2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Qian, Yefei Ibrayim, Mayire Hamdulla, Askar Zhang, Chunhu Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Computer Science and Technology Xinjiang University Xinjiang China

ISBN: (纸本)9798350360363

Although scene text recognition (STR) methods have made great progress, reading the text of irregularly shaped scenes remains a challenge. The current conversion of two-dimensional (2D) images to one-dimensional (1D) feature maps for sequence recognition may lose a large amount of spatial information and cannot recognize text with a large degree of curvature. In this paper, we propose an network infrastructure to recognize texts of arbitrary shapes, named COT2D Attention Text Recognition Network (COT2Net). Firstly, deformable convolution is added to the feature extraction module, which can perform feature extraction according to the shape of the character and expand the receptive field of feature extraction;Finally, in the Transformer encoding stage, the multi-head attention is changed to Contextual 2D Multi-head Attention (COT2D), which can make full use of the relationship between the input keys Q, K, and V to guide the learning of dynamic attention, thereby enhancing the visual expression ability of text images. The COT2DNet uses COT2D to learn the relevance of each character in a scene text image and achieves excellent results on public datasets. © 2023 IEEE.

关键词： Feature extraction

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Target detection algorithm with wide field of view in foggy weather based on improved CSLBP operator 4

Target detection algorithm with wide field of view in foggy ...

引用

4th International Conference on Image processing and Intelligent Control, IPIC 2024

作者： Lian, Xiya Jia, Zhenhong Zhou, Gang Wang, Jiajia Song, Sensen Xiao, Xiaohui Shi, Fei College of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510682313

In order to solve the problem of unclear foreground targets and false foregrounds after performing target detection tasks in foggy video images with wide fields of view, we propose an improved video target detection algorithm for the CSLBP operator to detect foreground targets in different scenes. The improved CSLBP operator is used to extract the texture features of the wide-field image, and the foreground target pixels are judged combined with background modeling. This method is not sensitive to changes in indoor lighting or outdoor sunlight, can effectively solve the shadow problem, and more completely extracts foreground moving targets. It also has obvious effects in bad weather such as foggy weather, and has good robustness. © 2024 SPIE.

关键词： Image enhancement

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A Bit-Filp Joint Algorithm of Polar-SCMA Systems for mMTC

A Bit-Filp Joint Algorithm of Polar-SCMA Systems for mMTC

引用

2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Qiao, Wei Ge, Wenping Zhang, Shiwei Sun, Yin Xinjiang University Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350331790

The conflict between limited spectrum resources and the need for massive machine-type communications (mMTC) is becoming more significant. To address these challenges, Sparse Code Multiple Access (SCMA) has emerged as a promising solution for mMTC in 5G. SCMA allows many users to share time and frequency resources, increasing spectral efficiency and receiving increased attention in the mMTC domain. As a control channel coding scheme in 5G communication systems, polar codes (PC) offer dependable data transmission capabilities. This paper presents a joint algorithm for an uplink PC-SCMA receiver that yields a reasonable block error rate (BLER) performance and an overload rate of 150% for mMTC. Generally, the previous error bit leads to much decoding error propagation in the following information bits for the iterative algorithm. We propose a bit-flip-assisted joint iterative detection decoding (BF-JIDD) algorithm for the error propagation problem in JIDD. Based on the simulation results, it can be concluded that the BF-JIDD algorithm outperforms the JIDD algorithm in terms of BLER performance. © 2023 IEEE.

关键词： Spectrum efficiency

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BD-YOLO: Optimising Insect Imbalance Data detection Models

BD-YOLO: Optimising Insect Imbalance Data Detection Models

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Jian-Yi Jia, Zhen-Hong Zhou, Gang Wang, Jia-Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665410205

Vision-based insect detection is an important task in smart agriculture. Field-collected insect image datasets usually show severe imbalances in categories. Previous models have insufficient accuracy as well as low real-time performance for detecting unbalanced data of insects. To address the issues described above, an improved framework Balance Detect YOLO(BD-YOLO) based on YOLOv8 is proposed in this paper. We innovatively designed Focaler-CPIoU loss to increase the weight of a few classes of samples to achieve the optimisation of pest imbalance data detection. We introduced lightweight convolutional MBConv to improve the recognition accuracy while reducing the number of model parameters. In order to verify the effectiveness of the method in this paper, we established an insect dataset containing six types of insects, in which the most numerous thrips is 431.5 times more than the least numerous chrysopaperla. The experimental results show that compared with YOLOv8, the mAP50 reaches 61.7% with a 73.1% reduction in the number of model parameters, an improvement of 5.4%, and an improvement of 769 in FPS. A feasible approach to optimise the detection of unbalanced number of insects is provided in this paper along with the ability to be deployed on lightweight hardware devices. © 2024 IEEE.

关键词： Invertebrates

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Enhancing Relational Classification Model Fusing Entity and Sentence Semantic Information 4

Enhancing Relational Classification Model Fusing Entity and ...

引用

4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Wei, Chunji Qin, Xizhong Yuan, Taiping Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi830017 China

ISBN: (纸本)9798350324303

Relationship classification aims at mining the relationship between two entities in a sentence and is an essential basis for the information extraction task. However, traditional relationship classification methods, which only use the information of words in a sequence to predict the relationship between entities, cannot use the semantic information of entities and sentences for relationship classification. To solve these problems, this paper proposes a new model, ES-BERT: this model uses the word-to-word association in the BERT model to obtain the semantic information of entities by the positional encoding of entity pairs in a sentence. In addition, we use the rich semantic knowledge in the internal hidden layer of Bert to obtain the semantic information of the sentence, which can better help the model understand the sentence. Finally, the semantic information of entities and the semantic information of sentences in the hidden layer of BERT are fused, and the fused information is used for relationship classification. The experimental results show that the model proposed in this paper achieves an excellent F1 value of 88.87% on the SemEva1-2010 Task 8 dataset, which is better than other Bert-based Sota models. © 2023 IEEE.

关键词： Classification (of information)

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Small target detection algorithm for flapping wing UAV based on improved YOLOv8 2

Small target detection algorithm for flapping wing UAV based...

引用

2nd International Symposium on Computer Applications and Information Systems, ISCAIS 2023

作者： Ma, Chen Du, Ketao Hamdulla, Askar School of Information Science and Engineering Xinjiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang Urumqi China

ISBN: (纸本)9781510666665

This paper proposes an algorithm based on an improved version of YOLOv8l, which is designed for small target detection on flapping wing drones. By adding a small object detection layer and introducing Multi-head self-attention (MHSA), the algorithm effectively reduces interference from irrelevant backgrounds and enhances the network’s feature extraction performance. Experimental results on both the flapping wing dataset and the VisDrone dataset demonstrate that compared with the baseline YOLOv8l algorithm, the improved algorithm shows a 4.1% and 3.3% improvement in the mAP@.5 and mAP@.5:.95 indicators, respectively, and a 5.9% and 4% improvement on the VisDrone dataset. Particularly noteworthy is the improved algorithm’s performance on the mAP@.5 index, which achieved 50.1% on the VisDrone dataset, proving its robustness and exceptional performance in small target detection. These results illustrate the algorithm’s effectiveness and practicality, making it a valuable tool for flapping wing UAV vision applications. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Slice Position-shifting and Multi-scale Inter-frame Proportional fusion Network for video-based pedestrian re-identification 2

Slice Position-shifting and Multi-scale Inter-frame Proporti...

引用

2nd International Symposium on Computer Applications and Information Systems, ISCAIS 2023

作者： Du, Ketao Ma, Chen Hamdulla, Askar School of Information Science and Engineering Xinjiang University Urumqi830046 China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang Urumqi China

ISBN: (纸本)9781510666665

Different from image-based pedestrian re-identification, video-based pedestrian re-identification has more available information because of more temporal information. However, previous methods are unable to extract detailed features adequately. Although the previous methods adopted the row slicing method to extract the detailed features of the image, the slicing method was too simple and the detailed features extracted were too few. We propose a slice position shifting module (SPM), which adopts a variety of ways to slice the image, to extract the feature of each image patches separately. The feature interaction is carried out by making each patches of the image generate position-shifting. How to make time clues in pedestrian re-identification data set useful is also the research focus in this field. We propose a multi-scale inter-frame proportional fusion module (MIPM) to make the features of frame t fully fuse with the front and back frames, so as to extract more the temporal clues in the data set. We will form the above two modules into Slice position-shifting and Multi-scale inter-frame proportional fusion network(SMnet) .Full experiments on the MARS dataset show that our algorithm is very useful and can reach the leading level. © 2023 SPIE.

关键词： Image processing

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Unsupervised wide field-of-view video image change detection

Unsupervised wide field-of-view video image change detection

引用

2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Liu, Rui Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

Video surveillance requires simultaneous monitoring of multiple areas. Consequently, real-time automatic change detection of the monitored areas becomes very important. In the context of wide field-of-view conditions, the combination of a wide field-of-view, intricate environmental factors, and a substantial presence of random noise can lead to the degradation of visual fidelity and a diminished signal-to-noise ratio in the video images acquired through the image sensor. As a consequence, the task of detecting subtle changes becomes challenging for the surveillance system. To address the above problems, we have proposed a change detection method that leverages improved difference images and super fast and robust fuzzy c-means with constraints clustering. Initially, we employ an improved log-ratio operator and an improved mean-ratio operator to generate two distinct difference images. Subsequently, the wavelet fusion algorithm is applied to merge these two difference images, effectively integrating their distinctive features and producing a fused difference image with differentiability. Then, the new difference image is subjected to soft threshold primary classification and a cumulative distribution function normalization to obtain the difference image after primary classification. Finally, the super fast and robust fuzzy c-means with constraints clustering algorithm is employed for the ultimate classification, enabling the separation of changed and unchanged areas within the image. © 2024 SPIE.

关键词： Clustering algorithms

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Coarse-to-fine change detection in low-illumination video sequences via classifier trained on ResNet18

Coarse-to-fine change detection in low-illumination video se...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Lin, Jiajun Jia, Zhenhong School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

In low-illumination environments, the contrast between targets and the background sharply decreases, and imaging sensors introduce complex random noise while capturing more light, leading to severe distortion in the target features of visible light images. Under these conditions, target feature-based detection algorithms face significant challenges. In this paper, we propose a coarse-to-fine change detection algorithm that detects targets in low-illumination environments by focusing on the change features generated by the targets. First, a binary classifier based on ResNet18 is trained with a specially curated dataset of targets and noise. Second, this classifier is applied to identify potential change objects in test data, preserve targets, and extract corresponding bi-temporal local regions of interest. Third, novel difference feature extraction operators are employed to generate local difference images. Next, a Laplacian-of-Gaussian-based graph cut algorithm is used to perform binary segmentation, distinguishing foreground from background in the images. We validated the feasibility of this algorithm in three challenging night scenes. Compared with the current state-of-the-art unsupervised change detection algorithms, the proposed algorithm shows overall better detection performance. © 2024 SPIE.

关键词： Change detection

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