scholarly journals Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yongqi Guo ◽  
Yuxu Lu ◽  
Yu Guo ◽  
Ryan Wen Liu ◽  
Kwok Tai Chui
Keyword(s):  
Target Detection ◽  
Water Surface ◽  
Data Augmentation ◽  
Weather Conditions ◽  
Detection Performance ◽  
Feature Representation ◽  
Maritime Transportation ◽  
Detection Accuracy ◽  
Visibility Enhancement ◽  
Degraded Images

The timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual image quality is often inevitably degraded due to the poor weather conditions, potentially leading to unsatisfactory target detection results. The degraded images could be restored using state-of-the-art visibility enhancement methods. It is still difficult to generate high-quality detection performance due to the unavoidable loss of details in restored images. To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. A data augmentation strategy, which synthetically simulates the degraded images under different weather conditions, is further presented to promote the generalization and feature representation abilities of our network. The proposed detection performance has the capacity of accurately detecting the water-surface targets under different adverse imaging conditions, e.g., haze, low-lightness, and rain. Experimental results on both synthetic and realistic scenarios have illustrated the effectiveness of the proposed framework in terms of detection accuracy and efficacy.

scholarly journals DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2743
Author(s):  
Kun Sun ◽  
Yi Liang ◽  
Xiaorui Ma ◽  
Yuanyuan Huai ◽  
Mengdao Xing
Keyword(s):  
Target Detection ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Cost ◽  
Detection Performance ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Computation Complexity ◽  
Learning Techniques

Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs).

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 Deep factor analysis for weather varied sense-through-foliage target detection

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Average Classification Accuracy ◽  
Weather Variation ◽  
The Impact

AbstractIn this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis (DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in hazy and snowy days, which can be stable at about 93%.

scholarly journals Deep Factor Analysis for Weather Varied Sense-through-foliage Target Detection

2020 ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Weather Variation ◽  
The Impact ◽  
Haze Days

Abstract In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis(DFA) in signal denoising are studied. To extensively verify the universality of the proposed DFA approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to further reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by the presented DFA, which can improve the average classification accuracy in all seasons. At the end of the paper, it is verified by cross validation that the DFA method can be stabilized at around 93% even in hazy day and snowy day which has stability and universality in any weather conditions, even in snowy and haze days.

scholarly journals Deep Factor Analysis for Weather Varied Sense-through-foliage Target Detection

2020 ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Weather Variation ◽  
The Impact ◽  
Haze Days

Abstract In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis(DFA) in signal denoising are studied. To extensively verify the universality of the proposed DFA approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to further reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by the presented DFA, which can improve the average classification accuracy in all seasons. At the end of the paper, it is verified by cross validation that the DFA method can be stabilized at around 93% even in hazy day and snowy day which has stability and universality in any weather conditions, even in snowy and haze days.

scholarly journals Deep Factor Analysis for Weather Varied Sense-through-foliage Target Detection

2021 ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Average Classification Accuracy ◽  
Weather Variation ◽  
The Impact

Abstract In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis(DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in foggy and snowy days, which can be stable at about 93%.

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

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.

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

Sign in / Sign up

Export Citation Format

Share Document