Target visibility under mesopic vision using a driving simulator

2018 ◽  
Vol 51 (6) ◽  
pp. 883-899 ◽  
Author(s):  
Z Chen ◽  
Y Tu ◽  
Z Wang ◽  
L Liu ◽  
L Wang ◽  
...  
Keyword(s):  
Target Detection ◽  
Driving Simulator ◽  
Target Position ◽  
Detection Performance ◽  
Small Target ◽  
Mesopic Vision ◽  
Road Lighting ◽  
Driving Workload ◽  
Target Visibility ◽  
The Impact

Small target visibility is widely used to evaluate the quality of road lighting. It provides a link between lighting design and driving performance. However, it is based on a strong simplification of the driving task using psychophysical data from laboratory conditions. Using a driving simulator to mimic the real driving environment, the impact of driving workload on target detection performance in mesopic vision conditions has been evaluated. The target visibility level is studied with and without driving workload together with different luminance contrasts and target positions, with reference to the small target visibility scenario. The results show that the driving workload significantly reduces the target detection performance. Consequently, the visibility level value for driving conditions should be much higher (visibility level ≥21) than some currently recommended ones (visibility level = 7) to achieve the same detection rates. Effects of target position and contrast are found in a way consistent with the literature. In addition, results indicate that the small target visibility model used for road lighting is limited and needs to be improved for a reliable prediction of visual performance with driving workload.

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

2010 ◽  
Vol 3 (4) ◽  
pp. 672-688 ◽  
Author(s):  
Tristan K. Cossio ◽  
K. Clint Slatton ◽  
William E. Carter ◽  
Kris Y. Shrestha ◽  
David Harding
Keyword(s):  
Target Detection ◽  
Detection Performance ◽  
Airborne Lidar ◽  
Small Target ◽  
Small Target Detection ◽  
Low Snr

scholarly journals Enhancing Small Moving Target Detection Performance in Low-Quality and Long-Range Infrared Videos Using Optical Flow Techniques

2020 ◽  
Vol 12 (24) ◽  
pp. 4024
Author(s):  
Chiman Kwan ◽  
Bence Budavari
Keyword(s):  
Optical Flow ◽  
Long Range ◽  
Target Detection ◽  
Moving Objects ◽  
Performance Enhancement ◽  
Detection Performance ◽  
Small Target ◽  
Air Turbulence ◽  
Analysis Center ◽  
Background Clutter

The detection of small moving objects in long-range infrared videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we summarize the investigation of efficient ways to enhance the performance of small target detection in long-range and low-quality infrared videos containing moving objects. In particular, we focus on unsupervised, modular, flexible, and efficient methods for target detection performance enhancement using motion information extracted from optical flow methods. Three well-known optical flow methods were studied. It was found that optical flow methods need to be combined with contrast enhancement, connected component analysis, and target association in order to be effective for target detection. Extensive experiments using long-range mid-wave infrared (MWIR) videos from the Defense Systems Information Analysis Center (DSIAC) dataset clearly demonstrated the efficacy of our proposed approach.

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar

2021 ◽  
Vol 13 (4) ◽  
pp. 701 ◽  
Author(s):  
Binbin Wang ◽  
Hao Cha ◽  
Zibo Zhou ◽  
Bin Tian
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
Time Integration ◽  
Detection Performance ◽  
Bistatic Radar ◽  
Detection Range ◽  
Robust Detection ◽  
Coherent Integration ◽  
Long Time ◽  
Long Time Integration

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.

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