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RCSLFNet: a novel real-time pedestrian detection network based on re-parameterized convolution and channel-spatial location fusion attention for low-resolution infrared image

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JOURNAL OF reAL-TIME IMAGE PROCESSING 2024年第3期21卷 89-89页

作者： Hao, Shuai Liu, Zhengqi Ma, Xu Wu, Yingqi He, Tian Li, Jiahao Xian Univ Sci & Technol Coll Elect & Control Engn Xian 710054 Peoples R China

A novel real-time infrared pedestrian detection algorithm is introduced in this study. The proposed approach leverages re-parameterized convolution and channel-spatial location fusion attention to tackle the difficulties presented by low-resolution, partial occlusion, and environmental interference in infrared pedestrian images. These factors have historically hindered the accurate detection of pedestrians using traditional algorithms. First, to tackle the problem of weak feature representation of infrared pedestrian targets caused by low resolution and partial occlusion, a new attention module that integrates channel and spatial is devised and introduced to CSPDarkNet53 to design a new backbone CSLF-DarkNet53. The designed attention model can enhance the feature expression ability of pedestrian targets and make pedestrian targets more prominent in complex backgrounds. Second, to enhance the efficiency of detection and accelerate convergence, a multi-branch decoupled detector head is designed to operate the classification and location of infrared pedestrians separately. Finally, to improve poor real-time without losing precision, we introduce the re-parameterized convolution (repconv) using parameter identity transformation to decouple the training process and detection process. During the training procedure, to enhance the fitting ability of small convolution kernels, a multi-branch structure with convolution kernels of different scales is designed. Compared with the nice classical detection algorithms, the results of the experiment show that the proposed RCSLFNet not only detects partial occlusion infrared pedestrians in complex environments accurately but also has better real-time performance on the KAIST dataset. The mAP@0.5 reaches 86% and the detection time is 0.0081 s, 2.9% higher than the baseline.

关键词： Infrared pedestrian detection real-time detection re-parameterized convolution Channel-spatial location fusion

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SAR Small Ship Detection Based on Enhanced YOLO Network

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reMOTE SENSING 2025年第5期17卷 839-839页

作者： Guan, Tianyue Chang, Sheng Wang, Chunle Jia, Xiaoxue Chinese Acad Sci Aerosp Informat Res Inst Space Microwave Remote Sensing Syst Dept Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Ships are important targets for marine surveillance in both military and civilian domains. Since the rise of deep learning, ship detection in synthetic aperture radar (SAR) images has achieved significant progress. However, the variability in ship size and resolution, especially the widespread presence of numerous small-sized ships, continues to pose challenges for effective ship detection in SAR images. To address the challenges posed by small ship targets, we propose an enhanced YOLO network to improve the detection accuracy of small targets. Firstly, we propose a Shuffle re-parameterization (SR) module as a replacement for the C2f module in the original YOLOv8 network. The SR module employs re-parameterized convolution along with channel shuffle operations to improve feature extraction capabilities. Secondly, we employ the space-to-depth (SPD) module to perform down-sampling operations within the backbone network, thereby reducing the information loss associated with pooling operations. Thirdly, we incorporate a Hybrid Attention (HA) module into the neck network to enhance the feature representation of small ship targets while mitigating the interference caused by surrounding sea clutter and speckle noise. Finally, we add the shape-NWD loss to the regression loss, which emphasizes the shape and scale of the bounding box and mitigates the sensitivity of Intersection over Union (IoU) to positional deviations in small ship targets. Extensive experiments were carried out on three publicly available datasets-namely, LS-SSDD, HRSID, and iVision-MRSSD-to demonstrate the effectiveness and reliability of the proposed method. In the small ship dataset LS-SSDD, the proposed method exhibits a notable improvement in average precision at an IoU threshold of 0.5 (AP50), surpassing the baseline network by over 4%, and achieving an AP50 of 77.2%. In the HRSID and iVision-MRSSD datasets, AP50 reaches 91% and 95%, respectively. Additionally, the average precision for small targets

关键词： synthetic aperture radar (SAR) small ship detection you only look once (YOLO) re-parameterized convolution

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SDGSAT-1 Cloud Detection Algorithm Based on RDE-SegNeXt

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reMOTE SENSING 2025年第3期17卷 470-470页

作者： Li, Xueyan Hu, Changmiao Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

This paper proposes an efficient cloud detection algorithm for Sustainable Development Scientific Satellite (SDGSAT-1) data. The core work includes the following: (1) constructing a SDGSAT-1 cloud detection dataset containing five types of elements: clouds, cloud shadow, snow, water body, and land, with a total of 15,000 samples;(2) designing a multi-scale convolutional attention unit (RDE-MSCA) based on a gated linear unit (GLU), with parallel re-parameterized convolution (repConv) and detail-enhanced convolution (DEConv). This design focuses on improving the feature representation and edge detail capture capabilities of targets such as clouds, cloud shadow, and snow. Specifically, the repConv branch focuses on learning a new global representation, reconstructing the original multi-branch deep convolution into a single-branch structure that can efficiently fuse channel features, reducing computational and memory overhead. The DEConv branch, on the other hand, uses differential convolution to enhance the extraction of high-frequency information, and is equivalent to a normal convolution in the form of re-parameterization during the inference stage without additional overhead;GLU then realizes adaptive channel-level information regulation during the multi-branch fusion process, which further enhances the model's discriminative power for easily confused objects. It is integrated into the SegNeXt architecture based on RDE-MSCA and proposed as RDE-SegNeXt. Experiments show that this model can achieve 71.85% mIoU on the SDGSAT-1 dataset with only about 1/12 the computational complexity of the Swin-L model (a 2.71% improvement over Swin-L and a 5.26% improvement over the benchmark SegNeXt-T). It also significantly improves the detection of clouds, cloud shadow, and snow. It achieved competitive results on both the 38-Cloud and LoveDA public datasets, verifying its effectiveness and versatility.

关键词： SDGSAT-1 cloud detection re-parameterized convolution detail-enhanced convolution convolutional attention

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Efficient forest fire detection based on an improved YOLO model

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Visual Intelligence 2024年第1期2卷 1-7页

作者： Cao, Lei Shen, Zirui Xu, Sheng Nanjing Forestry University Nanjing China

A forest fire is a natural disaster characterized by rapid spread, difficulty in extinguishing, and widespread destruction, which requires an efficient response. Existing detection methods fail to balance global and local fire features, resulting in the false detection of small or hidden fires. In this paper, we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions. We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model. Then, a re-parameterized convolutional module is designed, and a decoupled detection head is used to accelerate the convergence speed. Finally, a weighted bi-directional feature pyramid network (BiFPN) is introduced to merge feature information for local information processing. In the evaluation, we use the complete intersection over union (CIoU) loss function to optimize the multi-task loss for different kinds of forest fires. Experiments show that the precision, recall, and mean average precision are increased by 4.2%, 3.8%, and 4.6%, respectively, compared with the classic YOLO v5 model. In particular, the mAP@0.5:0.95 is 2.2% higher than the other detection methods, while meeting the requirements of real-time detection. © The Author(s) 2024.

关键词： Attention mechanism Decoupled detection head Deep learning Forest fire detection re-parameterized convolution

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A novel sequence-to-sequence based deep learning model for satellite cloud image time series prediction

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ATMOSPHERIC reSEARCH 2024年 306卷

作者： Lian, Jie Wu, Shixin Huang, Sirong Zhao, Qin Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata 100 Guilin Rd Shanghai 200030 Peoples R China

Satellite cloud imagery is pivotal for meteorologists in characterizing weather patterns, detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud image forecasting is crucial, and while deep learning models have shown promise in predicting spatio-temporal data, traditional methods face challenges with extracting long-term spatio-temporal features and high computation costs. To address these issues, we propose the re -parameterized Sequence -to -Sequence Satellite Cloud Imagery Prediction Network (repSSCIPN). rep-SSCIPN utilizes rep -convolution layers to reduce inference -time cost and memory consumption, enhancing efficiency by converting re -parameterized blocks into a single convolution layer during inference. The sequence normalization attention mechanism in rep-SSCIPN highlights crucial feature sequences and establishes their inter -dependencies. We validate our novel method using a real -world satellite cloud image dataset from the meteorological satellite "Himawari." Experimental results showcase significant improvements in prediction accuracy and reconstruction quality compared to ConvLSTM, PredRNN, FCLSTM, LMC, SimVP and SCSTque models. The efficiency gains make rep-SSCIPN a promising advancement for satellite cloud image prediction. ARTICLE INFO.

关键词： Satellite cloud image prediction Sequence-to-sequence model re-parameterized convolution Deep learning

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