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15 pages, 1389 KB

Open AccessArticle

Comparative Analysis of Real-Time Detection Models for Intelligent Monitoring of Cattle Condition and Behavior

by Oleg Ivashchuk, Zhanat Kenzhebayeva, Alexey Zhigalov, Moldir Allaniyazova, Gulnara Kaziyeva, Kaiyrbek Makulov, Vyacheslav Fedorov and Olga Ivashchuk

Algorithms 2025, 18(12), 763; https://doi.org/10.3390/a18120763 (registering DOI) - 2 Dec 2025

Abstract

This study benchmarks nine state-of-the-art object detection models on a specialized cattle dataset to assess accuracy and inference speed for real-time agricultural applications. Using a unified protocol without model-specific augmentations, and evaluating all detectors on identical RTX 4090 hardware, we provide a fair [...] Read more.

This study benchmarks nine state-of-the-art object detection models on a specialized cattle dataset to assess accuracy and inference speed for real-time agricultural applications. Using a unified protocol without model-specific augmentations, and evaluating all detectors on identical RTX 4090 hardware, we provide a fair architectural comparison of two-stage, one-stage, and transformer-based models. D_FINE_L and Co_DETR_R_50 achieved the highest accuracy (AP@[0.50:0.95] = 0.872 and 0.851), while RTMDet and YOLOv11_L were the fastest (15.81 and 19.14 ms/image). All models showed substantial accuracy gains on the domain dataset compared to COCO, while maintaining consistent relative speed rankings. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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19 pages, 10374 KB

Open AccessArticle

Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning

by Yicheng Qiu, Li Niu, Feng Sha, Zhaokun Cheng and Keiji Yanai

Algorithms 2025, 18(12), 736; https://doi.org/10.3390/a18120736 - 24 Nov 2025

Viewed by 150

Abstract

Neural network pruning is essential for deploying deep learning models on resource-constrained devices by reducing computational and memory demands. In this paper, we propose a novel pruning framework, Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning, which uses information entropy to assess [...] Read more.

Neural network pruning is essential for deploying deep learning models on resource-constrained devices by reducing computational and memory demands. In this paper, we propose a novel pruning framework, Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning, which uses information entropy to assess the importance of convolutional layers. Specifically, we calculate the layer-wise entropy of pretrained weights, apply outlier detection to remove extreme values, and normalize the entropy values. These normalized values guide the selection of retention ratios, ensuring that layers with higher entropy retain more filters. By refining the subnetwork search space, our approach enhances the efficiency of the search process and improves overall subnetwork performance. The refined search space targets more promising regions, reducing computational overhead and resulting in higher-quality pruned networks. Through iterative optimization, the optimal subnetwork is identified and fine-tuned to produce the final pruned model. Experimental results on benchmark datasets show that our method significantly outperforms existing pruning methods, achieving substantial improvements in both accuracy and computational efficiency. This entropy-guided pruning strategy provides a robust and effective solution for neural network compression, suitable for a wide range of deep learning applications. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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21 pages, 23184 KB

Open AccessArticle

FDC-YOLO: A Blur-Resilient Lightweight Network for Engine Blade Defect Detection

by Xinyue Xu, Fei Li, Lanhui Xiong, Chenyu He, Haijun Peng, Yiwen Zhao and Guoli Song

Algorithms 2025, 18(11), 725; https://doi.org/10.3390/a18110725 - 17 Nov 2025

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Abstract

The synergy between continuum robots and visual inspection technology provides an efficient automated solution for aero-engine blade defect detection. However, flexible end-effector instability and complex internal illumination conditions cause defect image blurring and defect feature loss, leading existing detection methods to fail in [...] Read more.

The synergy between continuum robots and visual inspection technology provides an efficient automated solution for aero-engine blade defect detection. However, flexible end-effector instability and complex internal illumination conditions cause defect image blurring and defect feature loss, leading existing detection methods to fail in simultaneously achieving both high-precision and high-speed requirements. To address this, this study proposes the real-time defect detection algorithm FDC-YOLO, enabling precise and efficient identification of blurred defects. We design the dynamic subtractive attention sampling module (DSAS) to dynamically compensate for information discrepancies during sampling, which reduces critical information loss caused by multi-scale feature fusion. We design a high-frequency information processing module (HFM) to enhance defect feature representation in the frequency domain, which significantly improves the visibility of defect regions while mitigating blur-induced noise interference. Additionally, we design a classification domain detection head (CDH) to focus on domain-invariant features across categories. Finally, FDC-YOLO achieves 7.9% and 3.5% mAP improvements on the aero-engine blade defect dataset and low-resolution NEU-DET dataset, respectively, with only 2.68 M parameters and 7.0G FLOPs. These results validate the algorithm’s generalizability in addressing low-accuracy issues across diverse blur artifacts in defect detection. Furthermore, this algorithm is combined with the tensegrity continuum robot to jointly construct an automatic defect detection system for aircraft engines, providing an efficient and reliable innovative solution to the problem of internal damage detection in engines. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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15 pages, 1171 KB

Open AccessArticle

Person Re-Identification Under Non-Overlapping Cameras Based on Advanced Contextual Embeddings

by Chi-Hung Chuang, Tz-Chian Huang, Chong-Wei Wang, Jung-Hua Lo and Chih-Lung Lin

Algorithms 2025, 18(11), 714; https://doi.org/10.3390/a18110714 - 12 Nov 2025

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Abstract

Person Re-identification (ReID), a critical technology in intelligent surveillance, aims to accurately match specific individuals across non-overlapping camera networks. However, factors in real-world scenarios such as variations in illumination, viewpoint, and pose continuously challenge the matching accuracy of existing models. Although Transformer-based models [...] Read more.

Person Re-identification (ReID), a critical technology in intelligent surveillance, aims to accurately match specific individuals across non-overlapping camera networks. However, factors in real-world scenarios such as variations in illumination, viewpoint, and pose continuously challenge the matching accuracy of existing models. Although Transformer-based models like TransReID have demonstrated a strong capability for capturing global context in feature extraction, the features they produce still have room for optimization at the metric matching stage. To address this issue, this study proposes a hybrid framework that combines advanced feature extraction with post-processing optimization. We employed a fixed, pre-trained TransReID model as the feature extractor and introduced a camera-aware Jaccard distance re-ranking algorithm (CA-Jaccard) as a post-processing module. Without retraining the main model, this framework refines the initial distance metric matrix by analyzing the local neighborhood topology among feature vectors and incorporating camera information. Experiments were conducted on two major public datasets, Market-1501 and MSMT17. The results show that our framework significantly improved the overall ranking quality of the model, increasing the mean Average Precision (mAP) on Market-1501 from 88.2% to 93.58% compared to using TransReID alone, achieving a gain of nearly 4% in mAP on MSMT17. This research confirms that advanced post-processing techniques can effectively complement powerful feature extraction models, providing an efficient pathway to enhance the robustness of ReID systems in complex scenarios. Additionally, it is the first-ever to analyze how the modified distance metric improves the ReID task when used specifically with the ViT-based feature extractor TransReID. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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28 pages, 4443 KB

Open AccessArticle

UCINet: A Multi-Task Network for Umbilical Coiling Index Measurement in Obstetric Ultrasound

by Zhuofu Liu, Lichen Niu, Zhixin Di and Meimei Liu

Algorithms 2025, 18(9), 592; https://doi.org/10.3390/a18090592 - 22 Sep 2025

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Abstract

The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which [...] Read more.

The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which suffers from low efficiency and susceptibility to inter-observer variability. In response to the challenges in measuring the umbilical coiling index during obstetric ultrasound, we propose UCINet, a multi-task neural network engineered explicitly for this purpose. UCINet demonstrates enhanced operational efficiency and significantly improved accuracy in detection, catering to the nuanced requirements of obstetric imaging. Firstly, this paper proposes a Frequency–Spatial Domain Downsampling Module (FSDM) to extract features in both the frequency and spatial domains, thereby reducing the loss of umbilical cord features and enhancing their representational capacity. The proposed Multi-Receptive Field Feature Perception Module (MRPM) employs receptive fields of varying sizes across different stages of the feature maps, enhancing the richness of feature representation. This approach allows the model to capture a more diverse set of spatial information, contributing to improved overall performance in feature extraction. A Multi-Scale Feature Aggregation Module (MSAM) comprehensively leverages multi-scale features via a dynamic fusion mechanism, optimizing the integration of disparate feature scales for enhanced performance. In addition, the UCI dataset, which consisted of 2018 annotated ultrasound images, was constructed, each labeled with the number of vascular coils and keypoints at both ends of the umbilical cord. Compared with state-of-the-art methods, UCINet achieves consistent improvements across two tasks. In object detection, UCINet outperforms Deformable DETR-R50 with an improvement of 1.2% points in mAP@50. In keypoint localization, it further exceeds YOLOv11 with a 3.0% gain in mAP@50, highlighting its effectiveness in both detection accuracy and fine-grained keypoint prediction. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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27 pages, 1902 KB

Open AccessArticle

Few-Shot Breast Cancer Diagnosis Using a Siamese Neural Network Framework and Triplet-Based Loss

by Tea Marasović and Vladan Papić

Algorithms 2025, 18(9), 567; https://doi.org/10.3390/a18090567 - 8 Sep 2025

Viewed by 787

Abstract

Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the [...] Read more.

Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the ability to secure timely and precise diagnostic results in breast cancer screening. AI technologies offer powerful tools that allow for the effective diagnosis and survival forecasting, reducing the dependency on human cognitive input. Towards this aim, this research introduces a deep meta-learning framework for swift analysis of mammography images—combining a Siamese network model with a triplet-based loss function—to facilitate automatic screening (recognition) of potentially suspicious breast cancer cases. Three pre-trained deep CNN architectures, namely GoogLeNet, ResNet50, and MobileNetV3, are fine-tuned and scrutinized for their effectiveness in transforming input mammograms to a suitable embedding space. The proposed framework undergoes a comprehensive evaluation through a rigorous series of experiments, utilizing two different, publicly accessible, and widely used datasets of digital X-ray mammograms: INbreast and CBIS-DDSM. The experimental results demonstrate the framework’s strong performance in differentiating between tumorous and normal images, even with a very limited number of training samples, on both datasets. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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21 pages, 3825 KB

Open AccessArticle

Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection

by Wenji Ai, Jiaxuan Zou, Zongchao Liu, Shaodi Wang and Shuai Teng

Algorithms 2025, 18(8), 462; https://doi.org/10.3390/a18080462 - 24 Jul 2025

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Abstract

Achieving state-of-the-art performance (82.5% IoU, 85.6% F1), this paper proposes an enhanced DeepLabV3+ model for robust underwater crack detection through three integrated innovations: a physics-based light propagation correction model for illumination distortion, multi-scale feature extraction for variable crack dimensions, and curvature flow-guided loss [...] Read more.

Achieving state-of-the-art performance (82.5% IoU, 85.6% F1), this paper proposes an enhanced DeepLabV3+ model for robust underwater crack detection through three integrated innovations: a physics-based light propagation correction model for illumination distortion, multi-scale feature extraction for variable crack dimensions, and curvature flow-guided loss for boundary precision. Our approach significantly outperforms DeepLabV3+, SCTNet, and LarvSeg by 10.6–13.4% IoU, demonstrating particular strength in detecting small cracks (78.1% IoU) under challenging low-light/high-turbidity conditions. The solution provides a practical framework for automated underwater infrastructure inspection. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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83 pages, 3818 KB

Open AccessSystematic Review

Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review

by Daniele Pelosi, Diletta Cacciagrano and Marco Piangerelli

Algorithms 2025, 18(7), 443; https://doi.org/10.3390/a18070443 - 18 Jul 2025

Cited by 4 | Viewed by 5545

Abstract

Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two distinct [...] Read more.

Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two distinct yet often conflated paradigms: explainable AI (XAI), which refers to post hoc techniques that provide external explanations for model predictions, and interpretable AI, which emphasizes models whose internal mechanisms are understandable by design. Meanwhile, the phenomenon of concept and data drift—where models lose relevance due to evolving conditions—demands renewed attention. High-impact events, such as financial crises or natural disasters, have highlighted the need for robust interpretable or explainable models capable of adapting to changing circumstances. Against this backdrop, our systematic review aims to consolidate current research on explainability and interpretability with a focus on concept and data drift. We gather a comprehensive range of proposed models, available datasets, and other technical aspects. By synthesizing these diverse resources into a clear taxonomy, we intend to provide researchers and practitioners with actionable insights and guidance for model selection, implementation, and ongoing evaluation. Ultimately, this work aspires to serve as a practical roadmap for future studies, fostering further advancements in transparent, adaptable machine learning systems that can meet the evolving needs of real-world applications. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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