Automatic Censoring Detection Using Binary Clutter-Map Estimation for NonGaussian Environments

Zattouta, B.; Farrouki, Atef; Barkat, M.

doi:10.1109/icspc.2007.4728291

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…To enhance operational efficiency and safety, there is a demand for FOD target detection algorithms at airports. Existing methods involve millimeter-wave radar [2] and vision-based approaches [3][4] . Millimeter-wave radar offers rapid scanning but is costly and unable to identify FOD types.…”

Section: Introductionmentioning

confidence: 99%

“…Addressing challenges in small target detection, three enhancements are made to YOLOv5s: (1) Fusion of multi-scale features introduces a new small target detection layer. (2) The CIoU loss function is improved with the introduction of the NWD loss function. (3) Ordinary convolutions in the detection network are replaced with ODConv full-dimensional dynamic convolutions, using a four-dimensional convolutional kernel with complementary attention in spatial space.…”

Section: Introductionmentioning

confidence: 99%

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Airport runway foreign object detection adapted to low-light environments

Chen,

Wen

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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To address the low accuracy of existing nighttime airport runway foreign object detection algorithms, this paper proposes the Low-Light YOLOv5s FOD algorithm by integrating low-light enhancement and YOLOv5 detection concepts. During the training of the low-light enhancement network, the brightness layer channel, normalized for brightness, is incorporated into the attention mechanism, allowing the model to focus more on dark areas. Subsequently, dark-light FOD images undergo low-light enhancement. On the YOLOv5s detection network, multiscale feature fusion, a small object detection layer, and the NWD loss function are employed to enhance small object detection and address position sensitivity issues. ODConv, a full-dimensional dynamic convolution, replaces the standard convolution, further improving accuracy with multidimensional complementary attention mechanisms. Finally, foreign object detection is performed on enhanced images. Experimental results indicate a 12.6% improvement in NIQE scores for the improved EnlightenGAN restored images and a 4.3% increase in mAP for the improved YOLOv5s compared to the original model. In nighttime environments, the proposed algorithm achieves an average detection accuracy of 99.39%, a 67.99% improvement over the original algorithm without low-light enhancement, at a detection speed of 50.30 frames/s. Balancing accuracy and real-time performance, this algorithm effectively addresses false positives and misses in FOD detection during nighttime conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Airport runway foreign object detection adapted to low-light environments

Chen,

Wen

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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“…In radar signal processing methods, the constant false alarm rate (CFAR) detection algorithm is commonly utilized for FOD detection. Cell average (CA) and ordered statistics (OS) CFAR methods have good detection performance with a homogeneous background, while the detection effect for small FOD targets is reduced with non-homogeneous backgrounds [13,14]. The clutter map (CM) CFAR algorithm can estimate the average clutter power through multiple iterations but the "self-shielding" is inevitable, leading to a reduction in detection performance [15].…”

Section: Introductionmentioning

confidence: 99%

Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

Chen

Tang

et al. 2020

Sensors

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Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.

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