- 标题
- 摘要
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- 实验方案
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An ultrasonic sensor composed of a fiber Bragg grating with an air bubble for underwater object detection
摘要： We present and experimentally demonstrate a novel optical fiber ultrasonic sensor composed of a fiber Bragg grating (FBG) with an air bubble for underwater object detection. The air bubble is formed by splicing etched FBG and single mode fiber (SMF) with taper-shaped holes. And because of the corrosive action, the diameters of FBG and SMF reduces from 125 μm to dozens of micrometers, which will be conducive to improving the sensor's performances. The experiment results indicate that the sensor has high voltage responses of 6.3 V and 7.1 V to the continuous and pulse ultrasonic wave (UW) at 1 MHz and could be used for underwater objects imaging with high signal-to-noise ratio (SNR). Subsequently, the environmental temperature will not influence ultrasonic measurements using the sensor on account of the low temperature sensitivity of 19.5 pm/°C.
关键词： Ultrasonic sensor,Fibre Bragg grating,Fibre-optic sensor,Underwater object detection
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments
摘要： Robust real-time object detection and tracking are challenging problems in autonomous transportation systems due to operation of algorithms in inherently uncertain and dynamic environments and rapid movement of objects. Therefore, tracking and detection algorithms must cooperate with each other to achieve smooth tracking of detected objects that later can be used by the navigation system. In this paper, we first present an efficient multi-sensor fusion approach based on the probabilistic data association method in order to achieve accurate object detection and tracking results. The proposed approach fuses the detection results obtained independently from four main sensors: radar, LiDAR, RGB camera and infrared camera. It generates object region proposals based on the fused detection result. Then, a Convolutional Neural Network (CNN) approach is used to identify the object categories within these regions. The CNN is trained on a real dataset from different ferry driving scenarios. The experimental results of tracking and classification on real datasets show that the proposed approach provides reliable object detection and classification results in maritime environments.
关键词： maritime environment,object detection,convolutional neural networks,region proposals,autonomous vessel,multi-sensor fusion
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Single Shot Feature Aggregation Network for Underwater Object Detection
摘要： The rapidly developing ocean exploration and observation make the demand for underwater object detection become increasingly urgent. Recently, deep convolutional neural networks (CNN) have shown strong ability in feature representation and CNN-based detectors also achieve remarkable performance, but still facing the big challenge when detecting multi-scale objects in a complex underwater environment. To address this challenge, we propose a novel underwater object detector, introducing multi-scale features and complementary context information for better classification and location ability. In the auto-grabbing contest of 2017 Underwater Robot Picking Contest sponsored by National Natural Science Foundation of China (NSFC), we won the 1-st place by using proposed method for real coastal underwater object detection.
关键词： context information,multi-scale features,underwater object detection,deep convolutional neural networks
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Las Vegas, NV (2018.4.8-2018.4.10)] 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - A NOVEL SEMI-SUPERVISED DETECTION APPROACH WITH WEAK ANNOTATION
摘要： In this work we propose a semi-supervised learning approach for object detection where we use detections from a preexisting detector to train a new detector. We differ from previous works by coming up with a relative quality metric which involves simpler labeling and by proposing a full framework of automatic generation of improved detectors. To validate our method, we collected a comprehensive dataset of more than two thousand hours of streaming from public traffic cameras that contemplates variations in time, location and weather. We used these data to generate and assess with weak labeling a car detector that outperforms popular detectors on hard situations such as rainy weather and low resolution images. Experimental results are reported, thus corroborating the relevance of the proposed approach.
关键词： traffic cameras,semi-supervised learning,object detection,weak annotation,quality metric
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Region of Interest Detection based on Local Entropy Feature for Disaster Victim Detection System
摘要： Region of interest (ROI) detection plays an important role in object detection. It needs to be accurate and fast in some applications like real time disaster victim detection systems. ROI can reduce time and search space in detecting objects. In this paper visual saliency map is used for ROI detection. In most literature, most of ROI detection models only concentrate on reducing false positive (detecting wrong objects as intended ones) rate rather than false negative (missing intended object). In disaster victim detection, missing disaster victims is more important than detecting other objects like victim. So, the proposed method also focuses on reducing false negative error rate in object detection. In the proposed system, local entropy feature is added in Graph Based Visual Saliency (GBVS) map in addition to colour, orientation and shape feature maps.
关键词： GBVS,local entropy feature map,ROI detection,object detection,false negative
更新于2025-09-04 15:30:14
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[ASME ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems - San Antonio, Texas, USA (Monday 10 September 2018)] Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies - Real-Time Detection of Ancient Architecture Features Based on Smartphones
摘要： Due to the particularity of texture features in ancient buildings, which refers to the fact that these features have a high historical and artistic value, it is of great significance to identify and count them. However, the complexity and large number of textures are a big challenge for the artificial identification statistics. In order to overcome these challenges, this paper proposes an approach that uses smartphones to achieve a real- time detection of ancient buildings’ features. The training process is based on SSD-Mobilenet, which is a kind of Convolutional Neural Network (CNN). The results show that this method shows well performance in reality and can indeed detect different ancient building features in real time.
关键词： real- time object detection,smartphone,ancient architecture feature,deep learning,convolution neural network,SSD-Mobilenet
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - Guangzhou, China (2018.10.8-2018.10.12)] 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks
摘要： Terahertz human body security images have low resolution and a low signal-to-noise ratio. In the traditional method, image segmentation, positioning, and identification are applied to detect objects carried by humans in the THz images. However, it is difficult to satisfy the requirements of detection accuracy and speed with this approach. The current research presents a faster detection framework (R-PCNN) combining the preprocessing and structure optimization of Faster R-CNN. The experiment results show that this method can effectively improve the accuracy and speed of object detection in human body THz images. A detection accuracy of 84.5% can be achieved in dense flow scenes, with an average detection time of less than 20 milliseconds for each image.
关键词： Image enhancement,Terahertz image,Faster R-CNN,Human body security check,Object detection
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Performance Evaluation of Flexible Electret Sensor Array for Ultrasonic Object Detection in Short Distance
摘要： Ultrasonic object detection in short distance could contribute the monitoring of human life activity and remote operation of home appliances. In this study, Ultrasonic sensor array using flexible electret elements were fabricated to detect objects in short distance. The reflection waves were clearly detected at short distance. The object (PVC pipe) detection was examined using the ultrasound generated by the ECS array. The estimated distance was good agreement with the true value though large errors were observed for the estimated angles because only three receivers were used for ECS array.
关键词： flexible sensor,electret,object detection,ultrasonic sensor,in-home activity recognition
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Kota Kinabalu, Malaysia (2018.10.7-2018.10.10)] 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Automated Planning of Rooftop PV Systems with Aerial Image Processing
摘要： The increasing prevalence of photovoltaic (PV) panels for microgrid and off-grid energy applications makes affordable PV planning an important issue. Since recording rooftop area and dimensions traditionally required on-site measurements, the process was expensive, slow, and hard to scale. This research develops software that uses image processing for roof detection. Satellite images feed into the software, which estimates the rooftop area receiving solar exposure in that area as well as the number of individual buildings receiving solar exposure. In this way, entire villages can be analyzed automatically, and PV installations planned from afar, rather than requiring a human taking measurements of each building from the ground. This research further develops a GUI to accomplish this rooftop classification for users around the globe, making this capability available even to parties with low resources who would benefit from access to electricity. In this way, the study makes planning PV systems feasible and affordable for many scales of installation, from a single home to a city of numerous assorted buildings.
关键词： demand forecasting,solar panels,object detection,photovoltaic systems,microgrids
更新于2025-09-04 15:30:14