Small Target Detection Algorithm Research Articles

P‐7.6: Research on Small Target Detection and Behavior Recognition Algorithm for Beach Monitoring System

P‐7.6: Research on Small Target Detection and Behavior Recognition Algorithm for Beach Monitoring System

Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure.

Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.

Intelligent Detection of Vehicle Driving Safety Based on Deep Learning

With the continuous increase in the number of cars, traffic safety problems are also becoming more and more serious, whether the driver wears a seat belt to protect the driver’s personal safety so that the problem can be solved in the event of a traffic accident. The author puts forward the research status of deep learning and convolutional neural network, as well as its theory and technology, and conducts in-depth analysis and research, a small target detection algorithm Deconv-SSD based on transposed convolution is proposed, driver area localization algorithm Squeeze-YOLO is based on lightweight model, and driver seat belt detection algorithm is based on semantic segmentation. Deconv-SSD achieves fast vehicle detection through depthwise separable convolution and fusion of multiresolution feature maps and then utilizes the salient features of the front windshield; through the method of lightweight feature extraction and the Squeeze-YOLO algorithm, the rapid positioning of the driver area is realized. Fast segmentation of seat belts is based on semantic segmentation algorithm and pruning technology in the positioning area, and by judging the maximum connected domain area after segmentation, the driver’s seat belt detection is realized. Experiments and data analysis are carried out on the proposed algorithm. When the image resolution is consistent with the feature extraction model, the average accuracy of Deconv-SSD is compared with the original SSD algorithm in the PASCALVOC public dataset, from 77.2% to 79.6%. In the self-made seat belt detection dataset, Squeeze-YOLO can reach 73 FPS when the average accuracy is 99.96%, the semantic segmentation algorithm accelerated by pruning achieves an accuracy of 94.87% at a speed of 305 FPS, and the validity of the experiment is verified.

Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm

In the face of complex scenes with strong edge contours and high levels of noise, suppressing edge contours and noise levels is challenging with infrared dim and small target detection algorithms. Many advanced algorithms suffer from high false alarm rates when facing this problem. To solve this, a new anisotropic background feature weight function based on the infrared patch tensor (IPT) model was developed in this study to characterize the background airspace difference features by effectively combining the local features with the global features to suppress the strong edge contours in the structural tensor. Secondly, to enhance the target energy in the a priori model, an improved high-order cumulative model was proposed to establish the local significance region of the target as a way to achieve energy enhancement of the significant target in the structural tensor. Finally, the energy-enhanced structural tensor was introduced into the partial sum of the sensor nuclear norm (PSTNN) model as a local feature information weight matrix; the detection results were obtained by solving the model with the help of ADMM. A series of experiments show that the algorithm in this paper achieves better detection results compared with other algorithms.

Infrared Small Target Detection Using Regional Feature Difference of Patch Image

Aiming at a thorny issue, that conventional small target detection algorithm using local contrast method is not sensitive for residual background clutter, robustness of algorithms is not strong. A Gaussian fusion algorithm using multi-scale regional patch structure difference and Regional Brightness Level Measurement is proposed. Firstly, Regional Energy Cosine (REC) is constructed to measure the structural discrepancy among a small target with neighboring cells. At the same time, Regional Brightness Level Measurement (RBLM) is constructed utilizing the brightness difference characteristics between small target and background areas. Then, a brand new Gaussian fusion algorithm is proposed for the generated saliency map in multi-scale space to characterize the overall heterogeneity in original infrared small target and local neighborhood. Finally, a self-adapting separation algorithm is adopted with the objective to obtain a small target from background interference. This method is able to utmostly restrain background interference and enhance the target. Extensive qualitative and quantitative testing results display that the desired algorithm has remarkable performance in strengthening target region and restraining background interference compared with current algorithms.

Research on infrared dim and small target detection algorithm based on frequency domain difference

ABSTRACT The high accuracy of infrared dim and small target detection in complex backgrounds is of great relevance for infrared identification and tracking systems. Traditional infrared dim and small target detection methods suit scenes with a single and homogeneous continuous background. However, human vision system methods suffer from an undetectable or high false alarm rate in complex scenes with dim small targets. To address this shortcoming, this paper proposes an infrared dim and small target detection algorithm based on frequency domain differencing (FDD). The proposed algorithm consists of a spectrum residual module and a Gaussian greyscale difference module. Firstly, the target enhancement image is constructed by using the spectrum residual module to highlight small targets and suppress background noise. Secondly, the local contrast of the image is enhanced by the Gaussian grayscale difference module, which accurately depicts the edge information of small targets and locates them. Finally, the target enhancement image and Gaussian grayscale difference image are fused to detect infrared dim and small targets. Experimental results show that the proposed algorithm has higher accuracy under the evaluation metrics of local signal-to-noise ratio gain (LSNRG), signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF). At the same time, compared with other algorithms, the detection rate of the proposed algorithm is higher for infrared dim and small targets in complex scenes. Code is available at https://github.com/m156879/FDD-module.

Infrared Small Target Detection Algorithm Based on Local Spatial Gradient Peak

A sparse infrared small target detection algorithm based on local spatial gradient peaks is proposed to deal with the problem of slow running speed and edge sensitivity in low-rank decomposition methods.The detection steps are as follows. In the first step, the image expansion operation is used for preprocessing. We use the circular structure element to sharpen the edges of targets and smooth the background noise. Then, the saliency gradient features of the target local region are applied to calculate the overlapping gradient information of the image after expansion. The local area with a larger gradient peak is located in the original image, and the selected local area is considered to be the region of interest with candidate targets. Finally, we use the advanced accelerated proximal gradient algorithm to perform matrix decomposition in the extracted local regions of interest to extract sparse infrared small targets. Extensive experimental results under real scenarios illustrated that compared with the baseline low-rank sparse decomposition method, the proposed approach runs faster and shows superior detection performance in the comprehensive evaluation index.

Statistical Calculation of Dense Crowd Flow Antiobscuring Method considering Video Continuity

People flow statistics have important research value in areas such as intelligent security. Accurately identifying the occluded target in video surveillance is a difficulty in the video surveillance system. Now the popular moving object tracking algorithm is based on detection and cannot accurately determine the relationship between overlapping. For the statistics of people flow in the video surveillance system, a dense crowd flow antiocclusion statistical algorithm considering video continuity is proposed. This study focuses on the improved faster R-CN algorithm for small target detection, moving target correlation matching, and two-way human flow intelligent statistics. According to the small-scale characteristics of the human head target, the faster R-CNNV network structure is adaptively improved. The shallow images features are used to improve the feature extraction ability of the network for small targets. The occlusion relationship function is constructed to clearly express the relationship between the occlusion targets, and it is incorporated into the framework of the tracking algorithm. A tracking algorithm based on trajectory prediction is used to follow moving targets in real time, and a two-way human flow intelligent statistical method is used to accomplish human flow. To prove the strength of the method, tests are managed in scenes with different degrees of density, and the results show that the improved target detection algorithm improves the average accuracy of 7.31% and 10.71% on the Brainwash test set and Pets2009 benchmark data set, respectively, compared with the original algorithm. The F-value of the comprehensive evaluation index of video stream of people intelligent statistical method in various scenes can reach more than 90%. Compared with the excellent methods SSD sorting algorithm and yolov3 deepsort algorithm in recent years, its F value is increased by 1.14%–3.04%.

Double layer local contrast measure and multi-directional gradient comparison for small infrared target detection

Infrared small target detection is one of the key technologies in the search and track (IRST) based on infrared imaging equipment. At present, the performance of small target detection based on single frame infrared image is directly related to the accuracy of subsequent target tracking, so it has been studied a lot. However, the existing small target detection algorithms have certain limitations in detection accuracy and real-time performance, especially when the contrast between the target and the background area is not high or the background is complex, especially in the complex sea or sky background, due to the influence of a large amount of noise and clutter in the background, the existing infrared small target detection algorithms have a high false alarm rate. To solve the above problems, this paper proposes a small target detection algorithm based on weighted double layer local contrast and multi-directional gradient map, which realizes the accurate detection of small targets from two aspects of targets’ local contrast and gradient. Firstly, we design an improved two layer local contrast measurement architecture, and use the weighted mean method to better represent the gray value of the local window; Secondly, a local contrast comparison method based on target and background is proposed to enhance the intensity of small targets and suppress some background clutter; Then, the multi-directional gradient map is used to further suppress the noise so as to improve the contrast between the target and the background. At the same time, singular value decomposition (SVD) method is used to extract the main features including small targets, which can effectively suppress the small texture interference around the targets in the background without losing the target intensity; Finally, an adaptive threshold method is used to separate small targets from their background. Experimental results show that compared with the existing algorithms, the proposed detection algorithm can effectively reduce the false alarm rate in different complex scenes, and the computational efficiency is improved compared with some multi-scale small target detection methods. At the same time, the signal to clutter ratio (SCR), background suppression factor (BSF) and receiver operating characteristic (ROC) curve are also better than these existing state of the art algorithms, which can display good robustness.

A Target Detection Algorithm for Remote Sensing Images Based on a Combination of Feature Fusion and Improved Anchor

Aiming at the low average target accuracy in remote sensing images with complex background and multiple small targets, we propose a new algorithm for small target detection based on a combination of feature fusion and improved anchor. Firstly, a data enhancement method is presented to increase the number of small targets in remote sensing images. Secondly, the high level features and low level features are efficiently fused using pixel-by-pixel summation and lightweight feature extraction, which can extract features that are more favorable for small target detection. Finally, by adjusting the aspect ratio of the anchor, the misdetection rate of small targets is reduced. The algorithm proposed in this paper is applied on the PASCAL VOC data set and UCAS-AOD data set, with the mean average accuracy (mAP) of 82.5% and 87.9%, respectively. The test results demonstrate that the proposed method can not only achieve a comparable detection speed but also demonstrates considerable accuracy superiority over traditional algorithms.

Lightweight Target Detection Algorithm for Small and Weak Drone Targets

Lightweight Target Detection Algorithm for Small and Weak Drone Targets

Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks

Under the explosive growth of “low-slow-small” unmanned aerial vehicles (UAVs), harassment incidents with “low-slow-small” UAVs, such as illegal surveying and mapping, no-fly zone flights, terrorist attacks, have frequently been occurring due to the low entry barrier to the industry and the lack of market norms and control standards, and severely impair the safety of public life and property. The premise of developing anti-UAV technology is efficient and accurate UAV target detection, which plays a vital role in the follow-up UAV target tracking, attack, and interception. This paper proposes a new DEAX algorithm named enhanced adaptive feature pyramid networks-based Target Detection Algorithm for infrared small UAV target detection. Our proposed algorithm improves the original feature pyramid networks in four aspects for the small UAV target detection task. 1) Use channel separation to keep more channels when adjusting the number of channels in convolutional to avoid the loss of helpful feature information; 2) Design a feature enhancement module to enhance “target feature” and suppress “non-target feature”; 3) Alleviate the differences of receptive fields and semantic information among different layers by shared convolution; 4) Introduce adaptive feature fusion method into feature fusion and complete the construction of enhanced adaptive feature pyramid networks (EAFPN) to solve the problem of weakening feature expression in cross-scale feature fusion. EAFPN is applied to the single-stage target detection networks. We find the detection accuracy and speed of the algorithm outperform those based on feature pyramid networks (FPN), where the improvement on mAP is 8.9%.

Infrared Small Target Detection Network With Generate Label and Feature Mapping

In the complex background, the contrast and signal-to-noise (SNR) ratio of the infrared (IR) small target are low. Therefore, the traditional IR small target detection algorithms are difficult to achieve good detection performance when the characteristics of small targets are sparse. To solve this problem, an IR small target detection network with generate label and feature mapping (GLFM)-net is proposed in this letter. First, in the GLFM-net model, a scale adaptive feature extraction network is proposed for the IR small target sparse features extraction, and then, the multilayer joint upsampling feature mapping network is proposed for small target feature mapping and background suppression. Based on this model, the feature mapping results of IR dim and small targets with the greatly suppressed background are obtained. Second, in model training, we designed a 2-D Gaussian label generation strategy for the problem of sample imbalance, which can achieve excellent detection performance by using small training samples. The experimental results show that the network can detect IR small targets with different sizes and low SNRs in various complex backgrounds and has good effectiveness and robustness compared with the existing algorithms.

Small Target Detection Algorithm Based on Multi-Stage Feature Fusion in Remote Sensing Image

Small Target Detection Algorithm Based on Multi-Stage Feature Fusion in Remote Sensing Image

Total Variation-Based Interframe Infrared Patch-Image Model for Small Target Detection

Infrared (IR) small target detection is one of the most fundamental techniques in the infrared search and track (IRST) system. Due to the interferences caused by background clutter and image noise, conventional IR small target detection algorithms always suffer from a high false alarm rate and are unable to achieve robust performance in complex scenes. To accurately distinguish IR small target from the background, we propose a total variation (TV)-based interframe infrared patch-image model that regards the long-distance IR small target detection task as an optimization problem. First, the input IR image is converted to a patch-image that consists of a sparse target matrix and a low-rank background matrix. Then, the interframe similarity of target appearance is utilized to impose a temporal consistency constraint on the target matrix. Next, a TV regularization term is proposed to further alleviate the false alarms generated by noise. Finally, an alternating optimization algorithm using singular value decomposition (SVD) and accelerated proximal gradient (APG) is designed to mathematically solve the proposed model. Both qualitative and quantitative experiments implemented on real IR sequences demonstrate that our model outperforms other traditional IR small target methods in terms of the signal-to-clutter ratio gain (SCRG) and the background suppression factor (BSF).

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm’s effectiveness in this paper.

Infrared Small Target Detection Based on Smoothness Measure and Thermal Diffusion Flowmetry

Infrared (IR) small target detection in a complex environment has become a hot topic. Due to the dim intensity and small scale of the target and the strong background clutter, the existing algorithms cannot achieve satisfactory results, namely poor detection performance and poor real-time performance. In this letter, a new real-time small target detection algorithm based on smoothness measure and thermal diffusion (STD) is proposed. First, the smoothness measure is proposed to extract smoothness features of small targets and suppress the clutter background. Then, we improve the existing thermal diffusion equation and design a thermal diffusion operator to extract the target’s thermal diffusion feature. The operator will further enhance the small and dim target’s energy. Finally, the fusion result image—in which the background is suppressed and the target is enhanced—is acquired after combining smoothness and thermal diffusion flowmetry. The experimental results demonstrate that the detection speed of the proposed algorithm is at least four times faster than that of existing algorithms under the same detection conditions. In addition, the proposed algorithm can achieve outstanding detection performance for IR images with different types of complex backgrounds and targets’ sizes within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\times7$ </tex-math></inline-formula> .

J-MSF：A new infrared dim and small target detection algorithm based on multi-channel and multiscale

J-MSF：A new infrared dim and small target detection algorithm based on multi-channel and multiscale

Dim and Small Target Detection Based on Spiral Gradient Optimization Estimation and High-Order Correlation Enhancement

In order to reduce the influence of strong halo effect on dim small target detection in daytime, a dim small target detection algorithm based on spiral gradient optimization estimation and high-order correlation enhancement is proposed in this paper. In this paper, we first design a spiral motion model to obtain the local gradient information in the central image point by perturbing the designed motion direction, then estimate the optimal background by establishing a gradient optimization model to achieve background suppression while effectively removing the halo phenomenon. Considering the original high-order correlation model only uses a single pixel for motion correlation, there is insufficient information utilization, an improved high-order correlation energy enhancement model is proposed to enhance the target signal, the algorithm first constructs an attention discrimination model based on inner and outer windows to obtain the salient region of the image, and then carries out multi frame high-order motion correlation of neighborhood blocks to enhance the target energy. After experiments, it is shown that compared with the traditional algorithm, the algorithm proposed in this paper can effectively weaken the halo effect while suppressing most of the background and can effectively enhance the local signal-to-noise ratio of the target.

An Improved Lightweight Small Target Detection Algorithm for Air-to-Ground Images

An Improved Lightweight Small Target Detection Algorithm for Air-to-Ground Images