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Qiang Zhao

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9 papers

2 author rows

EAAI Journal 2026 Journal Article

The accidental explosion tracing model of architecture glass damage based on shuffle attention

Hao Liu
Zhen Qing Wang
Shuai Qin
Qiang Zhao
Lei Zhang

Explosion tracing is the basis of hazard analysis and risk analysis of accidental explosion. The accidental explosion damage effects data has typical multi-source heterogeneous characteristics. The method of data fusion can be used to aggregate the redundant or complementary information on multiple sensors to obtain the more completed information. The architecture glass damage accidental explosion tracing machine learning model with Shuffle attention based on the experiment and simulate data of tempered glass plate under blast loading has been exhibited. Two branch models, blast wave propagation tracing model (Process-model) and glass plate dynamic response tracing model (Response-model), were established based on multi-source data fusion. Then the final decision tracing model (Decision-model) has been constructed based on the decision level. The Mean Absolute Percentage Error (MAPE) of the Decision-model was reduced to 0. 1605 compared with two branch models. The data of three different positions of the glass plate were used in the tracing process respectively. The results indicated that the data of the peripheral area showed the largest error. Considering the incompleteness of the actual explosion accident investigation data, in order to verify the real-word practical application of the model, an accidental explosion verification test of emulsion explosive was carried out. The MAPE of the actual measured imperfect dataset is 0. 2423. The results show that the tracing model can still ensure its prediction accuracy even when part input data is missing in practical applications. It provides a reliable analysis tool for accident explosion risk assessment.

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AAAI Conference 2025 Conference Paper

Region-aware Difference Distilling with Attribute-guided Contrastive Regularization for Change Captioning

Rong Li
Liang Li
Jiehua Zhang
Qiang Zhao
Hongkui Wang
Chenggang Yan

Change captioning aims to describe the differences between two similar images using natural language, significantly aiding in understanding and monitoring changes. This challenging task requires a fine-grained understanding of subtle changes while resisting disturbances like viewpoint shifts and illumination variations. Existing methods often rely solely on global difference features and lack comprehensive alignment of linguistic and visual information, leading to overlooking fine-grained details and generating semantic hallucinated sentences. To address these limitations, we propose the region-aware difference distilling (RDD) network with attribute-guided contrastive regularization (ACR). The RDD uses global difference features to progressively distill regional difference features using learnable vectors, allowing for more precise identification of changed regions. The ACR enhances comprehensive alignment between linguistic and visual information by formulating Nouns-to-Objects (N2O) and Verbs-to-Actions (V2A) alignment losses to regularize the regional difference features. Promising results on three datasets demonstrate that our method outperforms the state-of-the-art change captioning methods.

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IROS Conference 2024 Conference Paper

MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps

Hang Wu
Zhenghao Zhang
Siyuan Lin
Xiangru Mu
Qiang Zhao
Ming Yang 0002
Tong Qin 0001

Robust localization is the cornerstone of autonomous driving, especially in challenging urban environments where GPS signals suffer from multipath errors. Traditional localization approaches rely on high-definition (HD) maps, which consist of precisely annotated landmarks. However, building HD map is expensive and challenging to scale up. Given these limitations, leveraging navigation maps has emerged as a promising low-cost alternative for localization. Current approaches based on navigation maps can achieve highly accurate localization, but their complex matching strategies lead to unacceptable inference latency that fails to meet the real-time demands. To address these limitations, we introduce MapLocNet, a novel transformer-based neural re-localization method. Inspired by image registration, our approach performs a coarse-to-fine neural feature registration between navigation map features and visual bird’s-eye view features. MapLocNet substantially outperforms the current state-of-the-art methods on both nuScenes and Argoverse datasets, demonstrating significant improvements in localization accuracy and inference speed across both single-view and surround-view input settings. We highlight that our research presents an HD-map-free localization method for autonomous driving, offering a costeffective, reliable, and scalable solution for challenging urban environments.

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EAAI Journal 2024 Journal Article

Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers: A novel hybrid metaheuristic algorithm

Xuesong Zhang
Anping Fu
Changshu Zhan
Duc Truong Pham
Qiang Zhao
Tiangang Qiang
Mohammed Aljuaid
Chenxi Fu

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA–II–SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA–II–SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings.

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IJCAI Conference 2023 Conference Paper

Sph2Pob: Boosting Object Detection on Spherical Images with Planar Oriented Boxes Methods

Xinyuan Liu
Hang Xu
Bin Chen
Qiang Zhao
Yike Ma
Chenggang Yan
Feng Dai

Object detection on panoramic/spherical images has been developed rapidly in the past few years, where IoU-calculator is a fundamental part of various detector components, i. e. Label Assignment, Loss and NMS. Due to the low efficiency and non-differentiability of spherical Unbiased IoU, spherical approximate IoU methods have been proposed recently. We find that the key of these approximate methods is to map spherical boxes to planar boxes. However, there exists two problems in these methods: (1) they do not eliminate the influence of panoramic image distortion; (2) they break the original pose between bounding boxes. They lead to the low accuracy of these methods. Taking the two problems into account, we propose a new sphere-plane boxes transform, called Sph2Pob. Based on the Sph2Pob, we propose (1) an differentiable IoU, Sph2Pob-IoU, for spherical boxes with low time-cost and high accuracy and (2) an agent Loss, Sph2Pob-Loss, for spherical detection with high flexibility and expansibility. Extensive experiments verify the effectiveness and generality of our approaches, and Sph2Pob-IoU and Sph2Pob-Loss together boost the performance of spherical detectors. The source code is available at https: //github. com/AntXinyuan/sph2pob.

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AAAI Conference 2022 Conference Paper

Unbiased IoU for Spherical Image Object Detection

Feng Dai
Bin Chen
Hang Xu
Yike Ma
Xiaodong Li
Bailan Feng
Peng Yuan
Chenggang Yan

As one of the fundamental components of object detection, intersection-over-union (IoU) calculations between two bounding boxes play an important role in samples selection, NMS operation and evaluation of object detection algorithms. This procedure is well-defined and solved for planar images, while it is challenging for spherical ones. Some existing methods utilize planar bounding boxes to represent spherical objects. However, they are biased due to the distortions of spherical objects. Others use spherical rectangles as unbiased representations, but they adopt excessive approximate algorithms when computing the IoU. In this paper, we propose an unbiased IoU as a novel evaluation criterion for spherical image object detection, which is based on the unbiased representations and utilize unbiased analytical method for IoU calculation. This is the first time that the absolutely accurate IoU calculation is applied to the evaluation criterion, thus object detection algorithms can be correctly evaluated for spherical images. With the unbiased representation and calculation, we also present Spherical CenterNet, an anchor free object detection algorithm for spherical images. The experiments show that our unbiased IoU gives accurate results and the proposed Spherical CenterNet achieves better performance on one real-world and two synthetic spherical object detection datasets than existing methods.

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IJCAI Conference 2021 Conference Paper

Bipartite Matching for Crowd Counting with Point Supervision

Hao Liu
Qiang Zhao
Yike Ma
Feng Dai

For crowd counting task, it has been demonstrated that imposing Gaussians to point annotations hurts generalization performance. Several methods attempt to utilize point annotations as supervision directly. And they have made significant improvement compared with density-map based methods. However, these point based methods ignore the inevitable annotation noises and still suffer from low robustness to noisy annotations. To address the problem, we propose a bipartite matching based method for crowd counting with only point supervision (BM-Count). In BM-Count, we select a subset of most similar pixels from the predicted density map to match annotated pixels via bipartite matching. Then loss functions can be defined based on the matching pairs to alleviate the bad effect caused by those annotated dots with incorrect positions. Under the noisy annotations, our method reduces MAE and RMSE by 9% and 11. 2% respectively. Moreover, we propose a novel ranking distribution learning framework to address the imbalanced distribution problem of head counts, which encodes the head counts as classification distribution in the ranking domain and refines the estimated count map in the continuous domain. Extensive experiments on four datasets show that our method achieves state-of-the-art performance and performs better crowd localization.

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IJCAI Conference 2018 Conference Paper

Distortion-aware CNNs for Spherical Images

Qiang Zhao
Chen Zhu
Feng Dai
Yike Ma
Guoqing Jin
Yongdong Zhang

Convolutional neural networks are widely used in computer vision applications. Although they have achieved great success, these networks can not be applied to 360 spherical images directly due to varying distortion effect. In this paper, we present distortion-aware convolutional network for spherical images. For each pixel, our network samples a non-regular grid based on its distortion level, and convolves the sampled grid using square kernels shared by all pixels. The network successively approximates large image patches from different tangent planes of viewing sphere with small local sampling grids, thus improves the computational efficiency. Our method also deals with the boundary problem, which is an inherent issue for spherical images. To evaluate our method, we apply our network in spherical image classification problems based on transformed MNIST and CIFAR-10 datasets. Compared with the baseline method, our method can get much better performance. We also analyze the variants of our network.

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AIJ Journal 2006 Journal Article

Decomposition of structural learning about directed acyclic graphs

Xianchao Xie
Zhi Geng
Qiang Zhao

In this paper, we propose that structural learning of a directed acyclic graph can be decomposed into problems related to its decomposed subgraphs. The decomposition of structural learning requires conditional independencies, but it does not require that separators are complete undirected subgraphs. Domain or prior knowledge of conditional independencies can be utilized to facilitate the decomposition of structural learning. By decomposition, search for d-separators in a large network is localized to small subnetworks. Thus both the efficiency of structural learning and the power of conditional independence tests can be improved.

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