scholarly journals Application of Smart Sensor in Underwater Weak Object Detection and Positioning

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yangmei Zhang
Keyword(s):  
Object Detection ◽  
Magnetic Induction ◽  
Detection Method ◽  
Negative Impact ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Smart Sensor ◽  
Scene Segmentation ◽  
Underwater Positioning ◽  
Underwater Object

This paper is aimed at studying underwater object detection and positioning. Objects are detected and positioned through an underwater scene segmentation-based weak object detection algorithm and underwater positioning technology based on the three-dimensional (3D) omnidirectional magnetic induction smart sensor. The proposed weak object detection involves a predesigned U-shaped network- (U-Net-) architectured image segmentation network, which has been improved before application. The key factor of underwater positioning technology based on 3D omnidirectional magnetic induction is the magnetic induction intensity. The results show that the image-enhanced object detection method improves the accuracy of Yellow Croaker, Goldfish, and Mandarin Fish by 3.2%, 1.5%, and 1.6%, respectively. In terms of sensor positioning technology, under the positioning Signal-to-Noise Ratio (SNR) of 15 dB and 20 dB, the curve trends of actual distance and positioning distance are consistent, while SNR = 10   dB , the two curves deviate greatly. The research conclusions read as follows: an underwater scene segmentation-based weak object detection method is proposed for invalid underwater object samples from poor labeling, which can effectively segment the background from underwater objects, remove the negative impact of invalid samples, and improve the precision of weak object detection. The positioning model based on a 3D coil magnetic induction sensor can obtain more accurate positioning coordinates. The effectiveness of 3D omnidirectional magnetic induction coil underwater positioning technology is verified by simulation experiments.

scholarly journals Moving Objects Detection & Recognition using Hybrid Canny Edge Detection Algorithm in Digital Image Processing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 56-60
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Moving Objects Detection ◽  
Canny Edge ◽  
Objects Detection ◽  
Canny Edge Detection Algorithm ◽  
Day By Day

Recognition and detection of an object in the watched scenes is a characteristic organic capacity. Animals and human being play out this easily in day by day life to move without crashes, to discover sustenance, dodge dangers, etc. Be that as it may, comparable PC techniques and calculations for scene examination are not all that direct, in spite of their exceptional advancement. Object detection is the process in which finding or recognizing cases of articles (for instance faces, mutts or structures) in computerized pictures or recordings. This is the fundamental task in computer. For detecting the instance of an object and to pictures having a place with an article classification object detection method usually used learning algorithm and extracted features. This paper proposed a method for moving object detection and vehicle detection.

scholarly journals An Improved Object Detection Method using Feature Map Refinement and Anchor Optimization

2021 ◽  
Vol 15 ◽  
pp. 541-549
Author(s):  
Yuxia Wang ◽  
Wenzhu Yang ◽  
Tongtong Yuan ◽  
Qian Li
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Search Space ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Feature Map ◽  
Input Size ◽  
Fusion Operation ◽  
Different Levels

Lower detection accuracy and insufficient detection ability for small objects are the main problems of the region-free object detection algorithm. Aiming at solving the abovementioned problems, an improved object detection method using feature map refinement and anchor optimization is proposed. Firstly, the reverse fusion operation is performed on each of the object detection layer, which can provide the lower layers with more semantic information by the fusion of detection features at different levels. Secondly, the self-attention module is used to refine each detection feature map, calibrates the features between channels, and enhances the expression ability of local features. In addition, the anchor optimization model is introduced on each feature layer associated with anchors, and the anchors with higher probability of containing an object and more closely match the location and size of the object are obtained. In this model, semantic features are used to confirm and remove negative anchors to reduce search space of the objects, and preliminary adjustments are made to the locations and sizes of anchors. Comprehensive experimental results on PASCAL VOC detection dataset demonstrate the effectiveness of the proposed method. In particular, with VGG-16 and lower dimension 300×300 input size, the proposed method achieves a mAP of 79.1% on VOC 2007 test set with an inference speed of 24.7 milliseconds per image.

scholarly journals A Vertex Concavity-Convexity Detection Method for Three-Dimensional Spatial Objects Based on Geometric Algebra

2020 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Pengcheng Yin ◽  
Jiyi Zhang ◽  
Xiying Sun ◽  
Di Hu ◽  
Zhifeng Shi ◽  
...  
Keyword(s):  
Geometric Algebra ◽  
Detection Method ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Basic Algorithm ◽  
Outer Product ◽  
Spatial Objects ◽  
Detection Algorithms ◽  
Product Operation ◽  
Object Based

Vertex concavity-convexity detection for spatial objects is a basic algorithm of computer graphics, as well as the foundation for the implementation of other graphics algorithms. In recent years, the importance of the vertex concavity-convexity detection algorithm for three-dimensional (3D) spatial objects has been increasingly highlighted, with the development of 3D modeling, artificial intelligence, and other graphics technologies. Nonetheless, the currently available vertex concavity-convexity detection algorithms mostly use two-dimensional (2D) polygons, with limited research on vertex concavity-convexity detection algorithms for 3D polyhedrons. This study investigates the correlation between the outer product and the topology of the spatial object based on the unique characteristic that the outer product operation in the geometric algebra has unified and definitive geometric implications in space, and with varied dimensionality. Moreover, a multi-dimensional unified vertex concavity-convexity detection algorithm framework for spatial objects is proposed, and this framework is capable of detecting vertex concavity-convexity for both 2D simple polygons and 3D simple polyhedrons.

A Real-time Underwater Object Detection Algorithm for Multi-beam Forward Looking Sonar

2012 ◽  
Vol 45 (5) ◽  
pp. 306-311 ◽  
Author(s):  
Enric Galceran ◽  
Vladimir Djapic ◽  
Marc Carreras ◽  
David P Williams
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection Algorithm ◽  
Underwater Object ◽  
Forward Looking

scholarly journals Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Manhuai Lu ◽  
Liqin Chen
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Specular Reflection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
External Disturbances ◽  
Feature Enhancement ◽  
Art Object ◽  
Testing Dataset ◽  
Pattern Information

The accuracy of object detection based on kitchen appliance scene images can suffer severely from external disturbances such as various levels of specular reflection, uneven lighting, and spurious lighting, as well as internal scene-related disturbances such as invalid edges and pattern information unrelated to the object of interest. The present study addresses these unique challenges by proposing an object detection method based on improved faster R-CNN algorithm. The improved method can identify object regions scattered in various areas of complex appliance scenes quickly and automatically. In this paper, we put forward a feature enhancement framework, named deeper region proposal network (D-RPN). In D-RPN, a feature enhancement module is designed to more effectively extract feature information of an object on kitchen appliance scene. Then, we reconstruct a U-shaped network structure using a series of feature enhancement modules. We have evaluated the proposed D-RPN on the dataset we created. It includes all kinds of kitchen appliance control panels captured in nature scene by image collector. In our experiments, the best-performing object detection method obtained a mean average precision mAP value of 89.84% in the testing dataset. The test results show that the proposed improved algorithm achieves higher detecting accuracy than state-of-the-art object detection methods. Finally, our proposed detection method can further be used in text recognition.

scholarly journals Offset Detection of Grate Trolley’s Side Plate Based on YOLOv4

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yueming Wang ◽  
Zhenru Li ◽  
Liangrui Fang ◽  
Qi Li
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Video Frame ◽  
Economic Losses ◽  
Judgment Time ◽  
Side Plate ◽  
Position Information ◽  
Video Images ◽  
Sintering Plant

Side plate offset is one of the grate system faults. If it is not dealt with in time, some accidents will occur and economic losses will be made. Aiming at the problems like time-consuming, labour-wasting, and low intelligent by the side plate offset detection method manually, an autoside plate offset detection method is proposed, based on You Only Look Once version 4 (YOLOv4). Two cameras were fixed to collect the image information of the grate trolley’s side plate. With reference to the grate trolley’s operation, the offset judgment rules were set. YOLOv4 object detection algorithm was used to detect the side plate and trolley’s chassis frame in video frame images. A baseline was set according to the position information of the trolley’s chassis frame output by detection, and then, the position intervals between side plates and the baseline could be determined by calculation. According to the judgment rules, the scheme in this paper could detect the offset fault of the trolley’s side plate timely, and an alarm would be made automatically when faults are detected. Our video images of the trolley’s side plate were collected and sorted in Baogang Group sintering plant for testing. In this experiment, no error judgment was made, and the average detection and judgment time was 0.024 s. In this paper, rather than manually, the real-time automatic detection was realized to detect the offset fault of the trolley’s side plate so as to provide a new solution for offset detection of the grate trolley’s side plate.

scholarly journals Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

2021 ◽  
Vol 10 (11) ◽  
pp. 742
Author(s):  
Xiaoyue Luo ◽  
Yanhui Wang ◽  
Benhe Cai ◽  
Zhanxing Li
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Moving Object Detection ◽  
Image Space ◽  
Moving Object ◽  
Surveillance Video ◽  
Traffic Surveillance

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

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