Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…First, based on the YOLOv7-Tiny network, the MAFPN neck structure is used to replace the ELAN structure to achieve the multi-scale capture of semantic information of underwater sea treasures, and to enhance the UPA-YOLO model to accurately locate the targets of underwater sea treasures; second, the P2ELAN module is constructed and added to the backbone network, which makes use of the redundancy information in the feature map and dynamically adjusts the convolution kernel to adapt to data The P2ELAN module is added to the backbone network, using the redundant information in the feature map, dynamically adjusting the convolutional kernel to adapt to the lack of data, reducing the number of parameters in the model, and introducing the MSCA attention mechanism to inhibit the complex and changeable background features underwater, to improve the semantic feature extraction ability of the UPA-YOLO model for underwater targets, adding the MPDiou loss function to the improved algorithm model and completing the data validation of the detection model; finally, based on the TensorRT acceleration framework, the optimisation of the target detection Finally, based on the TensorRT acceleration framework, the target detection model is optimised, and the Jetson Nano edge device is used to complete the localisation deployment and realise the real-time target detection task of underwater sea treasures. …”
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YOLO-SMUG: An Efficient and Lightweight Infrared Object Detection Model for Unmanned Aerial Vehicles by Xinzhe Luo, Xiaogang Zhu

“…The model incorporates an enhanced backbone architecture that integrates the lightweight Shuffle_Block algorithm and the Multi-Scale Dilated Attention (MSDA) mechanism, enabling effective small object feature extraction while significantly reducing parameter size and computational cost without compromising detection accuracy. …”
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Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning by Berk Özel, Muhammad Shahab Alam, Muhammad Umer Khan

“…Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. …”
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MDCFVit-YOLO: A model for nighttime infrared small target vehicle and pedestrian detection. by Huiying Zhang, Qinghua Zhang, Yifei Gong, Feifan Yao, Pan Xiao

“…An MDCFVit-YOLO model based on the YOLOv8 algorithm is proposed to address issues in nighttime infrared object detection such as low visibility, high interference, and low precision in detecting small objects. …”
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Foreign object detection for mining conveyor belts based on YOLOv5n-CND by SUN Aoran, ZHAO Peipei, YANG Di, ZHANG Junyi, YU Hongjian

“…To address the issues of complex background in foreign object images, weak feature extraction, low detection accuracy for adhering small objects, and distortion in detection box positioning and scale, a foreign object detection algorithm for mining conveyor belts based on YOLOv5n-CND is proposed. …”
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Improved Early-Stage Maize Row Detection Using Unmanned Aerial Vehicle Imagery by Lulu Xue, Minfeng Xing, Haitao Lyu

“…This study proposed an enhanced Region of Interest (ROI)-based approach for detecting early-stage maize rows. It integrated a modified green vegetation index with a dual-threshold algorithm for background segmentation. …”
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The Development of a Lightweight DE-YOLO Model for Detecting Impurities and Broken Rice Grains by Zhenwei Liang, Xingyue Xu, Deyong Yang, Yanbin Liu

“…A rice impurity detection algorithm model, DE-YOLO, based on YOLOX-s improvement is proposed to address the issues of small crop target recognition and the similarity of impurities in rice impurity detection. …”
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HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text by Vanita Kshirsagar, Nishant Pachpor, Ashwini Brahme, Ravindra Aapre, Shubhangi Suryawanshi, Digvijay Bhosale

“…This paper introduces the Hybrid Ensemble Deep Learning model as novel for the task of the detection of sarcasm task, targeting the weaknesses which were found in traditional approaches of SVC, DT, RF, and LR by using unique combinations of CNN, LSTM, and GRU to capture the sarcasm patterns that appear fine feature representation and enhanced robustness and accuracy. …”
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An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository by Maram Aldiabat, Qussai M. Yaseen, Qusai Abu Ein

“…This paper proposes a machine learning model to assess the feasibility of employing machine learning algorithms for detecting the security status of Docker images available in the Docker Hub repository. …”
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Breast mass lesion area detection method based on an improved YOLOv8 model by Yihua Lan, Yingjie Lv, Jiashu Xu, Yingqi Zhang, Yanhong Zhang

“…The YOLOv8-P2 algorithm significantly reduces the number of necessary parameters by streamlining the number of channels in the feature map. …”
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Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy by Polina Lemenkova

“…The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. …”
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Block-Level Matching Recognition Algorithm for OpenStreetMap and Segments From High-Resolution Remote Sensing by Fan Shen, Jianyu Chen, Yu Shen, Zhao Gun, Qiankun Zhu

“…The effectiveness of segmentation algorithms influences the results, necessitating the selection of algorithms based on specific scenarios. …”
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A Spatio-Temporal Attention Network With Multiframe Information for Infrared Small Target Detection by Donghui Liu, Wenlong Zhang, Zicheng Feng, Xiaoliang Sun, Rui Zhang, Yang Shang

“…Most algorithms focus solely on extracting features from the spatial domain. …”
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SensorDBSCAN: Semi-Supervised Active Learning Powered Method for Anomaly Detection and Diagnosis by Petr Ivanov, Maria Shtark, Alexander Kozhevnikov, Maksim Golyadkin, Dmitry Botov, Ilya Makarov

“…The proposed approach combines a transformer-based encoder trained with a triplet-based contrastive learning objective and the classical density-based clustering algorithm DBSCAN, enabling strong feature extraction, efficient and interpretable feature space organization and simple clustering algorithm. …”
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DETECTION OF TEXT OBJECTS IN IMAGES OF REAL SCENES BASED ON CONVOLUTIONAL NEURAL NETWORK MODEL by N. N. Kuzmitsky

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Red-KPLS Feature Reduction with 1D-ResNet50: Deep Learning Approach for Multiclass Alzheimer’s Staging by Syrine Neffati, Ameni Filali, Kawther Mekki, Kais Bouzrara

“…The proposed method integrates discrete wavelet transform (DWT) for multi-scale feature extraction, a novel reduced kernel partial least squares (Red-KPLS) algorithm for feature reduction, and ResNet-50 for classification. …”
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Long and short term fault prediction using the VToMe-BiGRU algorithm for electric drive systems by Lihui Zheng, Xu Fan, Zongshan Kang, Xinjun Jin, Wenchao Zheng, Xiaofen Fang

“…Specifically, the VToMe algorithm achieves stable detection of medium to long term system faults, while the BiGRU network achieves rapid fault prediction in the short term. …”
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Eagle-YOLOv8: UAV Object Detection Inspired by the Eagle-Eye Vision System by Dianwei Wang, Zehao Gao, Jie Fang, Yuanqing Li, Zhijie Xu

“…However, the typically small size and uneven spatial distribution of objects in UAV imagery pose significant challenges for UAV object detection tasks. Confronting such challenges, we propose Eagle-YOLOv8, an object detection algorithm for UAV imagery inspired by the eagle-eye vision system. …”
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A network-based approach on detecting dredgers’ illegal behavior of dumping dredged sediments by Rongxin Zhang, Fei Yuan, En Cheng

“…Focusing on detecting the illegal behavior of dumping dredged sediments by dredgers at ports, a sensor network composed of two acoustic sources and several passive sensor arrays is proposed in this article. …”
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Modularized Learning for Hate Speech Detection in Korean: Integrating Emotions and Multi-Faceted Attributes by Hyeun Jeong Min

“…In this study, we present a modularized learning algorithm for hate speech detection in Korean that integrates diverse features, including emotional indicators and attributes capturing expressions of hatred. …”
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