Real-time Detection Algorithm for Small Space Targets Based on Max-median Filter

2014 ◽  
Vol 11 (4) ◽  
pp. 1047-1055 ◽  
Author(s):  
Yonghui Xu
Keyword(s):  
Real Time ◽  
Median Filter ◽  
Detection Algorithm ◽  
Small Space ◽  
Real Time Detection

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).

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