With the advent of functional brain imaging techniques and recent developments in the analysis of cortical connectivity, the focus of mental imagery studies has shifted from a semi-modular approach to a more realistic, integrated, cortical networks perspective. Recent studies of visual imagery of faces and objects suggest that activation of content-specific representations stored in the ventral visual stream is top-down modulated by parietal and frontal regions. The relation of these findings to other cognitive functions is discussed, as well as their clinical implications for patients with impaired states of conscious awareness.
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Sensors (Basel)
January 2025
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. Through a detailed comparison, their respective workflows, strengths, limitations, and applicability across different contexts are examined.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model-PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection.
View Article and Find Full Text PDFJ Imaging
January 2025
Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. In physical 3D environments, existing adversarial attacks that target object detection (3D-AE) face significant challenges. These attacks often require large and dispersed modifications to objects, making them easily noticeable and reducing their effectiveness in real-world scenarios.
View Article and Find Full Text PDFJ Imaging
January 2025
School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China.
For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface).
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