scholarly journals An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4026 ◽  
Author(s):  
Quan Sun ◽  
Zhaodong Niu ◽  
Weihua Wang ◽  
Haijing Li ◽  
Lang Luo ◽  
...  
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Nearest Neighbor ◽  
Detection Algorithm ◽  
Background Suppression ◽  
Main Work ◽  
Multi Target Tracking ◽  
Real Time Detection ◽  
Adaptive Ability ◽  
Operating Distance

Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is necessary to avoid collision accidents. Because radar is limited by transmitting power and operating distance, it is difficult to detect GSO debris, so photoelectric detection becomes the mainstream way to detect GSO debris. This paper presents an adaptive real-time detection algorithm for GSO debris in the charge coupled device (CCD) images. The main work is as follows: An image adaptive fast registration algorithm and an enhanced dilation difference algorithm are proposed. Combining with mathematical morphology, threshold segmentation and global nearest neighbor (GNN) multi-target tracking algorithm, the functions of image background suppression, registration, suspected target extraction and multi-target tracking are realized. The processing results of a large number of measured data show that the algorithm can detect dim geostationary earth orbit (GEO) and non-GEO debris in GSO belt stably and efficiently, and the processing speed meets the real-time requirements, with strong adaptive ability, and has high practical application value.

Small Target Real-Time Detection Algorithm Based on Improved MDSSD

2020 ◽  
Vol 57 (20) ◽  
pp. 201009
Author(s):  
奚琦 Xi Qi ◽  
张正道 Zhang Zhengdao ◽  
彭力 Peng Li
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Small Target ◽  
Real Time Detection

scholarly journals Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

2019 ◽  
Vol 9 (14) ◽  
pp. 2865 ◽  
Author(s):  
Kyungmin Jo ◽  
Yuna Choi ◽  
Jaesoon Choi ◽  
Jong Woo Chung
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Motion Vector ◽  
Detection Algorithm ◽  
Surgical Instruments ◽  
The Mean ◽  
Real Time Detection ◽  
Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

scholarly journals Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1504 ◽  
Author(s):  
Yu Zhou ◽  
Chunxue Wu ◽  
Qunhui Wu ◽  
Zelda Makati Eli ◽  
Naixue Xiong ◽  
...  
Keyword(s):  
Real Time ◽  
Working Conditions ◽  
Working Condition ◽  
Oil Well ◽  
Monitoring Method ◽  
The Real ◽  
Multi Target Tracking ◽  
Key Factor ◽  
Real Time Detection ◽  
Rotorcraft Uav

The traditional oil well monitoring method relies on manual acquisition and various high-precision sensors. Using the indicator diagram to judge the working condition of the well is not only difficult to establish but also consumes huge manpower and financial resources. This paper proposes the use of computer vision in the detection of working conditions in oil extraction. Combined with the advantages of an unmanned aerial vehicle (UAV), UAV aerial photography images are used to realize real-time detection of on-site working conditions by real-time tracking of the working status of the head working and other related parts of the pumping unit. Considering the real-time performance of working condition detection, this paper proposes a framework that combines You only look once version 3 (YOLOv3) and a sort algorithm to complete multi-target tracking in the form of tracking by detection. The quality of the target detection in the framework is the key factor affecting the tracking effect. The experimental results show that a good detector makes the tracking speed achieve the real-time effect and provides help for the real-time detection of the working condition, which has a strong practical application.

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