A Lightweight and Efficient Network for Logistics Truck Scene Semantic Segmentation

Liu, Shichen; Li, Baokun; Xiong, Jianping; Wang, Feng; Zhuo, Zhongshuo; Ren, Xinyi

doi:10.1109/iccc51575.2020.9345246

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…4) We generate a sauce-packet dataset to facilitate research. Furthermore, our method Mean Intersection over Union (mIoU) achieves 80.8% and Mean Pixel Accuracy (mPA) reaches 90.1% on the self-built dataset, which are +0.9% and +0.9% higher than the previous CNN method [18].…”

Section: Introductionmentioning

confidence: 85%

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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.

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Section: Introductionmentioning

confidence: 85%

“…In this section, we compare ISLS with several state-of-the-art (SOTA) semantic segmentation networks, including HRNet [13], BiseNetv2 [14], SegFormer [15], PSPNet [45], DeepLabv3 [46], and LIEPNet [18]. The evaluation results are shown in Table 3.…”

Section: Comparison With Other Segmentation Methodsmentioning

confidence: 99%