An efficient fire detection algorithm based on Mamba space state linear attention by Yuming Li, Yongjie Wang, Xiaorui Shao, Anbo Zheng

“…Abstract As an emerging State Space Model (SSM), the Mamba model draws inspiration from the architecture of Recurrent Neural Networks (RNNs), significantly enhancing the global receptive field and feature extraction capabilities of object detection models. Compared to traditional Convolutional Neural Networks (CNNs) and Transformers, Mamba demonstrates superior performance in handling complex scale variations and multi-view interference, making it particularly suitable for object detection tasks in dynamic environments such as in fire detection scenarios. …”
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SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Sarita Simaiya, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani, Afnan Alhazmi

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An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images by Sorio Boit, Rajvardhan Patil

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LMD_YOLO: A Lightweight and Efficient Model for Pavement Defects Detection by Shuai He, Ye Yuan, Bingyang Yin

“…These advancements enable LMD_YOLO to achieve high detection accuracy and robustness in complex conditions while maintaining computational efficiency. …”
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A Refined and Efficient CNN Algorithm for Remote Sensing Object Detection by Bingqi Liu, Peijun Mo, Shengzhe Wang, Yuyong Cui, Zhongjian Wu

“…RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. …”
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Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery by Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary

“…Vehicle detection in satellite images is a challenging task due to the variability in scale and resolution, complex background, and variability in object appearance. …”
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An efficient network for object detection in scale-imbalanced remote sensing images by Li Zhongyu, Jing Xiaoping, Sun Rui, Yang Yazhi, Wang Wei, Zhu Hongzhen

“…The model first introduces an improved frequency channel attention mechanism to design a feature extraction module to improve the extraction of key features by the neural network; second, considering that the complete intersection over union method does not comprehensively consider the aspect ratio of the bounding box, which will cause the loss of small-scale target feature information, the efficient intersection over union method is used to improve it; then, because of the high missed detection rate of the non-maximum suppression (NMS) method, Soft-EIoU-NMS is used to replace the original NMS method. …”
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A DFT insight into the potential of cycloparaphenylenes as efficient sensors for detecting Paracetamol by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

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RADNet: Adaptive Spatial-Dilation Learning for Efficient Road Crack Detection by Kehao Du, Yifan Dai

“…Road crack detection is crucial for infrastructure maintenance and traffic safety, yet existing methods struggle to balance detection accuracy and computational efficiency due to complex texture similarities between cracks and road surfaces. …”
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Angus: efficient active learning strategies for provenance based intrusion detection by Lin Wu, Yulai Xie, Jin Li, Dan Feng, Jinyuan Liang, Yafeng Wu

“…Besides, we also improve the above query strategies by using the parallel query to reduce detection time overheads. The experiments on various real-world applications demonstrate their performance and efficiency.…”
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ELNet: An Efficient and Lightweight Network for Small Object Detection in UAV Imagery by Hui Li, Jianbo Ma, Jianlin Zhang

“…In addition, since YOLOv12n employs standard convolution for downsampling, which is inefficient for extracting UAV image features, we design a novel downsampling module, EDown, to further reduce model size and enable more efficient feature extraction. Finally, to improve detection in UAV imagery with dense, small, and scale-varying objects, we propose DIMB-C3k2, an enhanced module built upon C3k2, which boosts feature extraction under complex conditions. …”
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PEYOLO a perception efficient network for multiscale surface defects detection by Xun Li, Yuzhen Zhao, Xiangke Jiao, Qingzhe Meng, Zhun Guo, Ruijuan Yao, Yaqiao Yang, Baoxi Yuan

“…To address this issue, we propose an innovative perception-efficient network designed for the fast and accurate detection of multi-scale surface defects. …”
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EcoDetect-YOLOv2: A High-Performance Model for Multi-Scale Waste Detection in Complex Surveillance Environments by Jing Su, Ruihan Chen, Mingzhi Li, Shenlin Liu, Guobao Xu, Zanhong Zheng

“…Even with surveillance imagery, challenges such as cluttered backgrounds, scale variation, and small object sizes often lead to missed detections and reduced robustness. To address these challenges, this study introduces EcoDetect-YOLOv2, a lightweight and high-efficiency object detection model developed using the Intricate Environment Waste Exposure Detection (IEWED) dataset. …”
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GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios by Shanshan Li, Zhe Zhang, Shijun Li

“…To address the demand for high-precision yet lightweight tea bud detection, this study proposes the GLS-YOLO detection model, based on YOLOv8. …”
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A review: Multi-Objective Algorithm for Community Detection in Complex Social Networks by Mariwan Wahid Ahmed, Kamaran Faraj

“…Recently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. …”
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Application of GPR Underground Pipeline Detection Technology in Urban Complex Geological Environments by Xiaoqiang Liang, Da Hu, Yongsuo Li, Yunyi Zhang, Xian Yang

“…This research applies bottom detection radar to urban underground pipeline detection technology under complex conditions for the first time, innovatively uses the action mechanism of bottom detection radar, integrates its high precision and high efficiency into underground pipeline detection technology, and ensures the effectiveness of the detection work.…”
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Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments by Jian Li, Wenkai Ma, Yanan Wei, Tan Wang

“…Previous approaches often struggle with complex agricultural environments where lighting conditions, pig postures, and crowding levels create challenging detection scenarios. …”
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Flexi-YOLO: A lightweight method for road crack detection in complex environments. by Jiexiang Yang, Renjie Tian, Zexing Zhou, Xingyue Tan, Pingyang He

“…Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. …”
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Infrared Ship Detection in Complex Nearshore Scenes Based on Improved YOLOv5s by Xiuwen Liu, Mingchen Liu, Yong Yin

“…Accurate ship identification and classification are central to this objective, underscoring the critical importance of ship detection technology. However, compared to open-sea surface, dense vessel distributions and complex backgrounds in nearshore areas substantially limit detection efficacy. …”
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Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

“…Experimental results show that the proposed YOLO-SSM algorithm has obvious advantages in accuracy and model complexity and can provide reliable theoretical support for efficient and accurate detection and identification of tea leaf diseases in natural scenes.…”
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