2021
DOI: 10.3390/s21082803
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A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

Abstract: The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detect… Show more

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Cited by 30 publications
(18 citation statements)
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References 36 publications
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“…According to the prediction evaluation, the proposed CNN-RF method is compared to some newly released mechanisms to detect damaged power lines with UAV and the IoT technologies including Convolutional Neural Network (CNN) [20], CNN [37] and Support Vector Machine (CNN-SVM) [23], Focal Phi Loss (FPL) [21,38], and convolutional features and structured constraints (CFSC) [25,39].…”
Section: Resultsmentioning
confidence: 99%
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“…According to the prediction evaluation, the proposed CNN-RF method is compared to some newly released mechanisms to detect damaged power lines with UAV and the IoT technologies including Convolutional Neural Network (CNN) [20], CNN [37] and Support Vector Machine (CNN-SVM) [23], Focal Phi Loss (FPL) [21,38], and convolutional features and structured constraints (CFSC) [25,39].…”
Section: Resultsmentioning
confidence: 99%
“…Another work [21] presented a generalized focal loss function established on the Matthews correlation coefficient to handle the class inequality issue in PL segmentation while using a generic deep segmentation structure. The loss function was assessed by enhancing the vanilla U-Net model with an extra convolutional auxiliary classifier head for more suitable learning and quick model conjunction.…”
Section: Related Workmentioning
confidence: 99%
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“…In [27], a deep CNN architecture with fully connected layers is proposed for PL segmentation, where the CNN inputs are histogram-of-gradient features -a sliding window is moved over each patch to get a classification of PL or not. In [28], a UNET architecture is trained to segment PLs based on a generalized focal loss function that uses the Matthews correlation coefficient [29] to address the class imbalance problem. In [22], an attentional convolutional network is proposed for pixel-level PL detection, and it consists of an encoder-decoder information fusion module and an attention module, where the former fuses the semantic information and the location information while the latter focuses on PLs.…”
Section: A Power Linesmentioning
confidence: 99%
“…Low-level segmentation of an image in non-overlapping set of regions called super-pixels helps in pre-processing and speeding up further high-level computational tasks related to visual images. The coherence feature of super-pixels allows faster architectural functionalities of many visual applications including object localization [ 1 ], tracking [ 2 ], posture estimation [ 3 ], recognition [ 4 , 5 ], semantic segmentation [ 6 ], instance segmentation [ 7 ], and segmentation of medical imagery [ 8 , 9 ]. These applications will be aided by super-pixels in terms of boosted performances, as the super-pixels put forward only the discriminating visual information [ 10 ].…”
Section: Introductionmentioning
confidence: 99%