Spectral–Spatial Feature Fusion for Hyperspectral Anomaly Detection

Liu, Shaocong; Li, Zhen; Wang, Guangyuan; Qiu, Xianfei; Liu, Tinghao; Cao, Jing; Zhang, Donghui

doi:10.3390/s24051652

Cited by 4 publications

(3 citation statements)

References 61 publications

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“…With the advancement of computer vision technology, leakage segmentation has become crucial for intelligent industrial production [1][2][3]. Sauce-packet leakage segmentation is to determine whether a sauce-packet has leakage [4].…”

Section: Introductionmentioning

confidence: 99%

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Preprint

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The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

show abstract

Section: Introductionmentioning

confidence: 99%

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This technological progression has allowed for HSIs that can describe the chemical constituents of terrestrial features with high precision. These images play a vital role in providing diagnostic spectral insights that can discriminate between distinct objects [5], rendering them invaluable for detection purposes [6][7][8][9][10][11]. However, owing to the spectral diversity intrinsic to natural substances and the limited spatial resolution of spectral sensors, small-sized objects may only occupy a fraction of an individual pixel, becoming enmeshed within the background [12,13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection

Yang,

Song,

Bai

et al. 2024

Remote Sensing

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Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual pixel, which significantly diminishes the utility of spatial information pertaining to the object. Therefore, the efficacy of detection algorithms depends heavily on the spectral data inherent in the image. The detection of subpixel objects in hyperspectral imagery primarily relies on the suppression of the background and the enhancement of the object of interest. Hence, acquiring accurate background information from HSI images is a crucial step. In this study, an adaptive background endmember extraction for hyperspectral subpixel object detection is proposed. An adaptive scale constraint is incorporated into the background spectral endmember learning process to improve the adaptability of background endmember extraction, thus further enhancing the algorithm’s generalizability and applicability in diverse analytical scenarios. Experimental results demonstrate that the adaptive endmember extraction-based subpixel object detection algorithm consistently outperforms existing state-of-the-art algorithms in terms of detection efficacy on both simulated and real-world datasets.

show abstract

“…With the advancement of computer vision technology, abnormal region segmentation has become crucial for intelligent industrial production [1][2][3]. Sauce-packet leakage segmentation is a typical abnormal region segmentation task.…”

Section: Introductionmentioning

confidence: 99%

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Sensors

View full text Add to dashboard Cite

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.

show abstract

Spectral–Spatial Feature Fusion for Hyperspectral Anomaly Detection

Cited by 4 publications

References 61 publications

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

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