Skinny: A Lightweight U-Net For Skin Detection And Segmentation

Tarasiewicz, Tomasz; Nalepa, Jakub; Kawulok, Michał

doi:10.1109/icip40778.2020.9191209

Cited by 18 publications

(11 citation statements)

References 31 publications

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“…This model also outperforms most CNN-based methods in terms of overall accuracy. While our method shows an accuracy of 96.33%, Tarasiewicz[41] (also a U-Net based architecture) reported an accuracy of 92%.…”

mentioning

confidence: 72%

“…Topiwala [42] has shown that U-Net stands out among the frequently-used skin detectors on their dataset of the human abdomen with different skin colors, The method based on U-Net was also computationally faster. Tarasiewicz [41] refined the U-Net architecture [34] by considering largescale contextual features, using inception and dense blocks to reduce occurrences of false positives significantly while doing skin detection.…”

Section: Related Work 21 Skin Detection For Natural Imagesmentioning

confidence: 99%

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Color Invariant Skin Segmentation

Han¹,

Sarkar²,

Abbott³

2022

Preprint

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This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, with the goal of color invariant feature learning to enhance generalization. We have demonstrated the concept using two architectures, and experimental results show improvements in both precision and recall for most Fitzpatrick skin tones in the benchmark ECU dataset. We further tested the system with the RFW dataset to show that the proposed method performs much more consistently across different ethnicities, thereby reducing the chance of bias based on skin color. To demonstrate the effectiveness of our work, extensive experiments were performed on grayscale images as well as images obtained under unconstrained illumination and with artificial filters.

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mentioning

confidence: 72%

Section: Related Work 21 Skin Detection For Natural Imagesmentioning

confidence: 99%

Color Invariant Skin Segmentation

Han¹,

Sarkar²,

Abbott³

2022

Preprint

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“…(i) U-Net is proposed by Ronneberger et al [14], which is a very popular one-stage image segmentation network [60,61]. The applied U-Net references the high-starred implementations on GitHub [62,63].…”

Section: The Pre-training Processmentioning

confidence: 99%

Rock Segmentation in the Navigation Vision of the Planetary Rovers

et al. 2021

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Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).

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“…Arsalan et al proposed OR-Skip-Net, an outer residual skin network for skin segmentation in nonideal situations [38]. Skinny, a lightweight U-net, is also introduced by Tarasiewicz for skin detection and segmentation [39]. Several skin-segmentation-related works are also discussed in a local texture-based gender classifier for smart phone application [40].…”

Section: Skin-color Segmentation and Edge Detectionmentioning

confidence: 99%

Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Quoc

Hoang

2021

Complexity

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Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and the current richness and variety of ear detection algorithms are poor due to the small and complex shape of human ears. In this paper, we introduce a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace is also added in the detection system to narrow the regions of interest and enhance the accuracy. The proposed method is evaluated on an unconstrained dataset, which shows its effectiveness.

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Skinny: A Lightweight U-Net For Skin Detection And Segmentation

Cited by 18 publications

References 31 publications

Color Invariant Skin Segmentation

Color Invariant Skin Segmentation

Rock Segmentation in the Navigation Vision of the Planetary Rovers

Real‐Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

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