Sensor-Based Image Processing and Sensing Techniques for Enhanced Object Detection
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 487
Special Issue Editor
Interests: embedded system; deep learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Object detection is a fundamental task in computer vision and image understanding, playing a critical role in a wide range of applications such as autonomous driving, intelligent surveillance, medical imaging, robotics, remote sensing, and industrial inspection. In recent years, rapid advances in image processing techniques—together with deep learning, multimodal data fusion, and edge intelligence—have significantly improved the accuracy, robustness, and efficiency of object detection systems.
Despite these advances, object detection in complex real-world environments remains challenging due to factors such as occlusion, illumination variation, background clutter, scale variation, noise, and limited computational resources. Advanced image processing methods continue to be essential for enhancing detection performance, improving generalization, and enabling real-time and resource-efficient deployment.
This Special Issue aims to bring together high-quality research contributions that explore novel image processing techniques and frameworks for enhanced object detection, from theoretical foundations to practical applications.
Dr. Hao Luo
Guest Editor
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Keywords
- advanced image preprocessing and enhancement techniques for object detection
- feature extraction, representation, and selection methods
- image segmentation and region proposal techniques
- deep learning-based object detection models and architectures
- lightweight and efficient detection algorithms for edge and embedded systems
- multi-scale, multi-view, and multi-modal object detection
- robust object detection under challenging conditions (e.g., low light, noise, occlusion)
- image fusion techniques for enhanced detection performance
- object detection in medical, industrial, remote sensing, and autonomous systems
- real-time and low-latency image processing for detection tasks
- explainable and interpretable image processing methods for object detection
- benchmark datasets, evaluation metrics, and comparative studies
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