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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Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8 by Runyi Lv, Jianping Hu, Tengfei Zhang, Xinxin Chen, Wei Liu

“…First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. …”
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Predicting the bounds of large chaotic systems using low-dimensional manifolds. by Asger M Haugaard

“…Here, a method is presented which treats extrema of chaotic systems as belonging to discretised manifolds of low dimension (low-D) embedded in high-dimensional (high-D) phase space. As a central feature, the method exploits that strange attractor dimension is generally much smaller than parent system phase space dimension. …”
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DSNET: A Lightweight Segmentation Model for Segmentation of Skin Cancer Lesion Regions by Yucong Chen, Guang Yang, Xiaohua Dong, Junying Zeng, Chuanbo Qin

“…This model achieves optimal segmentation performance while maintaining low model parameters and computational complexity. To reduce the model size and guarantee model segmentation performance, we proposed a detail-enhanced separable difference convolution as a base module in the model. …”
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A lightweight semantic segmentation method for concrete bridge surface diseases based on improved DeeplabV3+ by Zhiyuan Yu, Chunquan Dai, Xiaoming Zeng, Yunlong Lv, Haisheng Li

“…Abstract Due to the similar features of different diseases and insufficient semantic information of small area diseases in the surface disease image of concrete bridges, the existing semantic segmentation models for identifying surface diseases in concrete bridges suffer from problems such as large number of parameters, insufficient feature extraction, and low segmentation accuracy. …”
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A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling by Enze Zhang, Yan Li, Haifeng Lin, Min Xia

“…In this context, technology based on deep learning has made substantial breakthroughs in change-detection performance by automatically extracting high-level feature representations of the data. However, although the existing deep-learning models improve the detection accuracy through end-to-end learning, their high parameter count and computational inefficiency hinder suitability for real-time monitoring and edge device deployment. …”
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…SPD-Conv was introduced to replace the original convolutional layers to retain fine-grained information and reduce model parameters and computational costs, thereby improving the accuracy of disease detection. …”
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Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture by Jintao Liu, Alfredo Tolón-Becerra, José Fernando Bienvenido-Barcena, Xinting Yang, Kaijie Zhu, Chao Zhou

“…Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. …”
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YOLO-DLHS-P: A Lightweight Behavior Recognition Algorithm for Captive Pigs by Changhua Zhong, Hao Wu, Junzhuo Jiang, Chaowen Zheng, Hong Song

“…Firstly, the C2f-DRB structure is introduced at the Backbone position, and the sizeable convolutional kernel is used to extend the receptive field to enhance the spatial perception ability of the model, and to enhance the network’s ability to capture spatial information while maintaining the number of learnable parameters and computational efficiency; The LSKA attention mechanism is then introduced to be integrated into the SPPF module to construct the SPPF-LSKA structure, which significantly improves the ability of the SPPF module to aggregate features at multiple scales; Then, the downsampling at the Neck position is optimised to the HWD algorithm, which reduces the spatial resolution of the feature map while retaining more useful information and reduces the uncertainty of the information compared with the downsampling method of the baseline model; finally, the Shape-IoU is used to replace the original CIoU, which significantly improves the detection efficiency and accuracy of the model without increasing the extra computational burden. …”
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A Novel Dangerous Goods Detection Network Based on Multi-Layer Attention Mechanism in X-Ray Baggage Images by Xu Yang, Ting Lan, Yili Xu

“…Compared with some state-of-the-art methods, our network improves performance by 5–10% while reducing parameters and increasing computational efficiency. …”
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AnomLite: Efficient binary and multiclass video anomaly detection by Anna K. Zvereva, Mariam Kaprielova, Andrey Grabovoy

“…AnomLite is competitive due to its computational efficiency, requiring only 11 million parameters, and its robustness, achieving a ROC AUC of 0.99, Average Precision of 0.99 and F1-Score (Weighted) of 0.92 and outperforming comparable models in anomaly detection tasks. …”
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A Lightweight Barcode Detection Algorithm Based on Deep Learning by Jingchao Chen, Ning Dai, Xudong Hu, Yanhong Yuan

“…The EfficientViT block based on a linear self-attention mechanism is introduced into the backbone of the original model to enhance the model’s attention to barcode features. In the model’s neck, linear mapping and grouped convolution are used to improve the C2f module, and the ADown convolution block is utilized to modify the model’s downsampling, which reduces the model’s parameters and computational cost while improving the efficiency of model feature fusion. …”
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Spatial-Temporal Cooperative In-Vehicle Network Intrusion Detection Method Based on Federated Learning by Liu Tao, Zhao Xiyang

“…This paper proposes a spatial-temporal collaborative intrusion detection method for IVN based on federated learning (FL), aiming to address the limitations of traditional intrusion detection methods in data privacy protection, temporal modeling, and computational efficiency. The method employs an autoencoder (AE) to achieve feature compression, reducing data dimensionality and extracting core spatial features. …”
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DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments by Giovanni Pintore, Marco Agus, Alberto Signoroni, Enrico Gobbetti

“…This lightweight architecture requires fewer tunable parameters and computational resources than competing methods. …”
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Lightweight defocus deblurring network for curved-tunnel line scanning using wide-angle lenses by Shaojie Qin, Taiyue Qi, Xiaodong Huang, Xiao Liang

“…The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network. Additionally, the proposed feature refinement block captures the structurally similar features to enhance the image details, increasing the peak signal-to-noise ratio (PSNR). …”
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Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…Inspired by ARM CPU efficiency optimizations, the model uses depthwise separable convolutions and efficient activation functions to reduce parameter size. The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. …”
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Lightweight Infrared Small Target Detection Method Based on Linear Transformer by Bingshu Wang, Yifan Wang, Qianchen Mao, Jingzhuo Cao, Han Zhang, Laixian Zhang

“…The model consists of two parts: a multi-scale linear transformer and a lightweight dual feature pyramid network. It combines the strengths of a lightweight feature extraction module and the multi-head attention mechanism, effectively representing the small targets in the complex background at an economical computational cost. …”
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Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement by Junfeng Wang, Shenghui Huang, Zhanqiang Huo, Shan Zhao, Yingxu Qiao

“…DEP learns overexposure and underexposure corrections simultaneously by employing the ReLU activation function, inverting operation, and residual network, which can improve the robustness of enhancement effects under different exposure conditions while reducing network parameters. Experiments on the LOL-V1 dataset shows BiEnNet significantly increased PSNR by 8.6 $$\%$$ and SSIM by 3.6 $$\%$$ compared to FLW-Net, reduced parameters by 98.78 $$\%$$ , and improved computational speed by 52.64 $$\%$$ compared to the classical KIND.…”
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WTSM-SiameseNet: A Wood-Texture-Similarity-Matching Method Based on Siamese Networks by Yizhuo Zhang, Guanlei Wu, Shen Shi, Huiling Yu

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