A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…The SggNet adopts a classical encoder-decoder structure with MobileNet-V2 as the backbone, ensuring optimal parameter utilization. Furthermore, we design an Efficient Global Perception Module (EGPM) to capture global feature relationships and semantic cues through limited computational costs, enhancing the model’s ability to perceive salient objects in complex scenarios, and a Semantic-Guided Edge Awareness Module (SEAM) that leverages the semantic consistency of deep features to suppress background noise in shallow features, accurately predict object boundaries, and preserve the detailed shapes of salient objects. …”
Get full text

BurgsVO: Burgs-Associated Vertex Offset Encoding Scheme for Detecting Rotated Ships in SAR Images by Mingjin Zhang, Yaofei Li, Jie Guo, Yunsong Li, Xinbo Gao

“…Moreover, oriented bounding box-based detection methods often prioritize accuracy excessively, leading to increased parameters and computational costs, which in turn elevate computational load and model complexity. …”
Get full text

YOLOv5 garbage classification method with GhostNet by Li Yao, Hu Junguo, Le Yang

Get full text

A Lightweight Approach to Comprehensive Fabric Anomaly Detection Modeling by Shuqin Cui, Weihong Liu, Min Li

“…In order to solve the problem of high computational resource consumption in fabric anomaly detection, we propose a lightweight network, GH-YOLOx, which integrates ghost convolutions and hierarchical GHNetV2 backbone together to capture both local and global anomaly features. …”
Get full text

YOLOv10-kiwi: a YOLOv10-based lightweight kiwifruit detection model in trellised orchards by Jie Ren, Wendong Wang, Yuan Tian, Jinrong He

“…This replacement enables parallel processing and enhances feature extraction efficiency. By combining heterogeneous kernels in sequence, C2fDualHet captures both local and global features while significantly lowering parameter count and computational cost. …”
Get full text

A lightweight trichosanthes kirilowii maxim detection algorithm in complex mountain environments based on improved YOLOv7-tiny. by Zhongjian Xie, Xinwei Chen, Weilin Wu, Yao Xiao, Yuanhang Li, Yaya Zhang, ZhuXuan Wan, Weiqi Chen

“…However, the environmental characteristics of brightness variation, inter-plant occlusion, and motion-induced blurring during harvesting operations, detection algorithms face excessive parameters and high computational intensity. Accordingly, this study proposes a lightweight T.Kirilowii detection algorithm for complex mountainous environments based on YOLOv7-tiny, named KPD-YOLOv7-GD. …”
Get full text

A lightweight MHDI-DETR model for detecting grape leaf diseases by Zilong Fu, Lifeng Yin, Can Cui, Yi Wang

“…The original residual backbone network was improved using the MobileNetv4 network, significantly reducing the model’s computational requirements and complexity. Additionally, a lightSFPN feature fusion structure is presented, combining the Hierarchical Scale Feature Pyramid Network with the Dilated Reparam Block structure design from the UniRepLKNet network. …”
Get full text

Using Wearable Sensors for Sex Classification and Age Estimation from Walking Patterns by Rizvan Jawad Ruhan, Tahsin Wahid, Ashikur Rahman, Abderrahmane Leshob, Raqeebir Rab

Get full text

Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…EfficientNetB0 achieves high accuracy with fewer parameters through model scaling strategies, while Xception offers powerful feature extraction using deep separable convolutions. …”
Get full text

TFDense-GAN: a generative adversarial network for single-channel speech enhancement by Haoxiang Chen, Jinxiu Zhang, Yaogang Fu, Xintong Zhou, Ruilong Wang, Yanyan Xu, Dengfeng Ke

“…In particular, the Unet architecture, which comprises three main components, the encoder, the decoder, and the bottleneck, employs DenseBlock in both the encoder and the decoder to achieve powerful feature fusion capabilities with fewer parameters. …”
Get full text

MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection by Jianhua Ye, Pan Li, Yunda Zhang, Ze Guo, Shoujin Zeng, Youji Zhan

“…Additionally, the network’s computational GLOPS is 13.45 G, and the number of parameters is 46.92 M, which can meet the requirements for real-time detection. …”
Get full text

YOLO-GCOF: A Lightweight Low-Altitude Drone Detection Model by Wanjun Yu, Kongxin Mo

“…The YOLO-GCOF model outperforms the original YOLOv8n, as demonstrated by a 1.1% improvement in mAP@50, alongside reductions in parameter count, computational overhead, and model size by 60%, 49.4%, and 55.1%, respectively. …”
Get full text

Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers. by Kenta Ninomiya, Hidetaka Arimura, Wai Yee Chan, Kentaro Tanaka, Shinichi Mizuno, Nadia Fareeda Muhammad Gowdh, Nur Adura Yaakup, Chong-Kin Liam, Chee-Shee Chai, Kwan Hoong Ng

“…Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. …”
Get full text

Identification of glass eel capture equipment in the Yangtze River estuary based on high-spatial -resolution imagery and an improved YOLOv8 model by Pengfei Zhu, Weifeng Zhou

“…To avoid the false detection of small targets, we introduce the asymptotic feature pyramid network to replace the original detection head, and add a detection layer for small targets, which improves the accuracy but increases the parameters and computation volume. …”
Get full text

YOLOv8-OCHD: A Lightweight Wood Surface Defect Detection Method Based on Improved YOLOv8 by Zuxing Chen, Junjie Feng, Xueyan Zhu, Bin Wang

“…Secondly, a C2f_RVB module is designed, which uses the RepViTBlock technique to optimize feature representation and effectively reduce the number of model parameters. …”
Get full text

Question-matching approach based on gradual machine learning by Xuejian HE, Anqi CHEN, Zhiqiang GUO, Zhiru WANG, Qun CHEN

Get full text

DC-YOLO: an improved field plant detection algorithm based on YOLOv7-tiny by Wenwen Li, Yun Zhang

“…Finally, we decoupled the detection head to minimize conflicts between features from different tasks. The results show that applying the proposed method to corn and weed datasets, the detection accuracy of the model reaches 95.7% mean Average Precision (mAP@0.5), the computational effort of the model is 13.083 Giga Floating-point Operations (GFLOPs), and the parameter size is 5.223 Millon (M), which is better than the rest of the mainstream light-weight target detection model.…”
Get full text

CAFNet: Cross-Modal Adaptive Fusion Network With Attention and Gated Weighting for RGB-T Semantic Segmentation by Meili Fu, Huanliang Sun, Zhihan Chen, Lulin Wei

“…The experimental results show that CAFNet achieves a 60.1% mIoU on the MFNet dataset, which is 1.2% higher than that of EAEFNet (58.9% mIoU), and the computational cost (110.61G FLOPs) and parameter count (68.13 M) are also reduced by 25% and 66.1%, respectively. …”
Get full text

Efficient remote sensing image classification using the novel STConvNeXt convolutional network by Bo Liu, Chenmei Zhan, Cheng Guo, Xiaobo Liu, Shufen Ruan

“…It employs parameterized depthwise separable convolutions to reduce computational complexity and constructs a multi-level feature tree to facilitate cross-scale feature fusion. …”
Get full text

CAREC: Continual Wireless Action Recognition with Expansion–Compression Coordination by Tingting Zhang, Qunhang Fu, Han Ding, Ge Wang, Fei Wang

Get full text