Infrared Target Detection Algorithm Research Articles

YOLO-B:An infrared target detection algorithm based on bi-fusion and efficient decoupled.

The YOLO-B infrared target detection algorithm is proposed to address the problems of incomplete extraction of detailed features and missed and wrong detection of infrared targets by YOLOv5s. The algorithm improves the SPPF of YOLOv5s feature extraction network by proposing the CSPPF structure to increase the sensory field of the model. The Bifusion Neck structure is invoked to fuse the shallow location information with deep semantic information to enhance the feature extraction capability of the model. Taking fully into account the different information of concern for classification and localization, the efficient decoupled head is used as the prediction head of this algorithm, which reduces the latency while maintaining the accuracy. WIoUv3 loss is used as a bounding box regression loss function to reduce the harmful gradient generated by low-quality examples and reduce the competitiveness of high-quality anchor frames. Comparative experiments were conducted for each of the four improvement points, and the experimental results showed that each improvement point had the highest detection accuracy in the comparative experiments of the same category. All improvement points are fused in turn and ablation experiments are performed. The YOLO-B algorithm improves 1.9% in accuracy, 7.3% in recall, 3.8% in map_0.5, and 4.6% in map_0.5:0.95 compared to YOLOv5s. When compared with YOLOv7 and YOLOv8s, the proposed algorithm has better performance in terms of the number of parameters and detection accuracy.

Infrared target detection algorithm based on improved Faster R-CNN

Infrared target detection algorithm based on improved Faster R-CNN

Research on infrared target detection based on neural network fusion

Infrared technology can detect targets under special weather conditions, such as night, rain and fog. To improve the detection accuracy of vehicles, pedestrians and other targets in infrared images, an infrared target detection algorithm with fusion neural network is proposed. Firstly, we use Ghost convolution to replace the resunit unit of the convolution layer of the deep residual network layer in YOLOv5s, which can reduce the amount of parameters without losing accuracy. Then, the global channel attention (GCA) is added to the upper sampling layer, the detection accuracy of network is further improved by enhancing the characteristics of the overall goal. Also, the Channel Space Attention (CPA) space attention mechanism is added to the output end to obtain more accurate target location information. The infrared data set taken by the UAV is trained and tested. The accuracy rate of detection based on YOLOv5s and fusion neural network is 96.47%, the recall rate is 91.51%, and the F1 score is 94%, which is 7% higher than YOLOv5s. The results show that the target detection rate of infrared images is improved by proposed method, which has strong research value and broad application prospects.

Multi-stage morphological operators for small infrared target detection

Infrared small target detection is a challenging task. Despite the recent advances in the development of small infrared target detection algorithms, having a robust target detection algorithm with high probability of detection rate remains unaddressed. To this end, in this paper, a morphological top-hat transform is presented. The proposed method benefits from intrinsic high probability detection rate of classical top-hat transform, while the false alarm rate remains below the acceptable threshold. Also, the sensitivity to noise analysis demonstrates that the proposed method is robust against various noise intensities. The proposed method is tested on some real infrared images. The experiments show that the proposed method outperforms state-of-the-arts in both quantitative and qualitative manners.

PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform

Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone network are replaced with depthwise separable convolutions. In post-processing the images, the soft-non-maximum suppression (soft-NMS) algorithm is employed to subside the missed and false detection problems caused by the occlusion between targets. The accuracy of our model can finally reach 61.75%, while the total Params is only 9.3 M and GFLOPs is 11. At the same time, the inference speed reaches 87 FPS on NVIDIA GeForce GTX 1650 Ti, which can meet the requirements of the infrared target detection algorithm for the embedded deployments.

Infrared target detection method using adaptive feature fusion

As the key technology of low altitude airspace security and air attack, vision-based air target detection has better concealment and lower cost compared with radar, radio and other detection methods. Based on yolov3, this paper proposes an air infrared target detection algorithm using adaptive feature fusion, which can effectively improve the accuracy of air infrared multi-scale target detection. Firstly, the construction of air infrared target detection data set is completed, and the target characteristics in the data set are statistically analysed. Secondly, the output layer of backbone is adjusted, and the four times down sampling feature map is used to replace the eight times down sampling feature map to enhance the detection ability of small targets. Thirdly, aiming at the problem of weakening feature expression caused by cross-scale feature fusion in FPN, a pixel level feature adaptive fusion module is designed. Finally, two improved SPP modules are added to neck to enrich the expression ability of feature map through the fusion of local features and global features. Experimental results show that compared with yolov3, our algorithm improves the map from 86.53% to 90.26% while keeping the detection speed basically unchanged.

Real-time Infrared Target Detection Algorithm for Embedded System in Complex Scene

Real-time Infrared Target Detection Algorithm for Embedded System in Complex Scene

Object detection and tracking algorithms using brain-inspired model and deep neural networks

As the most effective bio-intelligence system, Human Visual System (HVS) has significant advantages in image processing, which helps to solve the problems in infrared target detection and tracking, such as dim small target, complex background, target occlusion and appearance changes, etc. In this paper, several brain-inspired models (including lateral inhibition, receptive field, synchronous burst, visual attention, and cognitive memory) and Deep Neural Networks (DNNs) have been studied, and the corresponding algorithms are proposed, which include: an infrared target detection algorithm based on lateral inhibition and singular value decomposition, an infrared target detection algorithm based on receptive field and lateral inhibition, an infrared moving dim target detection algorithm based on ALI-PCNN, an infrared target detection algorithm based on GCF-SB visual attention model, a kernel correlation filtering target tracking algorithm based on multi-channel memory model, and a robust and efficient discriminative-correlation-filter-based tracking approach based on the Response Map Analysis Network. Our experimental results show that the proposed algorithms are beneficial to achieve accurate infrared target detection and robust tracking under complex conditions.

Image Target Detection Algorithm of Smart City Management Cases

With the rapid development and wide application of Internet technology, the use of the concept of “smart city” has been concerned and promoted. It is an inevitable way to strengthen the breadth and depth of urban services, and to move forward from digital to intelligent application. The purpose of this study is to further promote the small and medium-sized cities in China to improve the “smart city” management mode and promote the harmonious development of society, which has important reference and practical significance. In this paper, in-depth analysis of the background of smart city, different image target detection algorithms are studied. The infrared target detection algorithm suppresses the background by means of a high-pass filter, and the coefficient of correlation between the characteristics is used as the fusion weight, while the weighted grey synthesis is performed, area and seroid offset. The ultra-spectral target detection algorithm extracts some content indicators from the initial data, and finally realizes the optimization of the algorithm. The mean filtering algorithm can reduce the effect of noise by pre-processing the image. The algorithm a hog-target detection describes the features of the object's surface edges in areas such as graphics and image processing; and calculates the distribution of characteristics in the direction of inclination of the particular part of the image. These algorithms have their own advantages and characteristics. The results of the experiment show that the accuracy and rate of recall of the infrared target detection algorithm after aggregation of characteristics are higher than other algorithms, the accuracy is higher than 6.3% of the original infrared image algorithm, and the recall rate is 5.4% higher than the infrared image anide algorithm. The change in the value m of the main vector dimension will affect the accuracy of target detection.

复杂天空背景下的红外弱小目标检测算法研究

复杂天空背景下的红外弱小目标检测算法研究

基于伪模态转换的红外目标融合检测算法

基于伪模态转换的红外目标融合检测算法

Small infrared target detection using absolute average difference weighted by cumulative directional derivatives

Infrared search and track (IRST) systems have attracted more attention in recent years due to advances of infrared imaging technology. In a typical IRST system, small infrared detection algorithm is the most challenging part of the system development procedure. In this paper, an effective algorithm based on the average absolute gray difference (AAGD) is presented. In the first step, the main deficiencies of the AAGD algorithm are investigated precisely. After identifying three major drawbacks of the AAGD algorithm, a powerful small target detection algorithm is developed through compensating every single weak-spot in the AAGD algorithm. The simulation results on real infrared images prove that the proposed algorithm not only compensates the AAGD disadvantages but also outperforms the recently published well-known small infrared target detection algorithms in both qualitative and quantitative perspectives.

复杂地面背景下的红外目标检测算法

复杂地面背景下的红外目标检测算法

An infrared target detection algorithm based on lateral inhibition and singular value decomposition

This paper proposes an infrared target detection algorithm based on lateral inhibition (LI) and singular value decomposition (SVD). Firstly, a local structure descriptor based on SVD of gradient domain is constructed, which reflects basic structures of the local regions of an infrared image. Then, LI network is modified by combining LI with the local structure descriptor for enhancing target and suppressing background. Meanwhile, to calculate lateral inhibition coefficients adaptively, the direction parameters are determined by the dominant orientations obtained from SVD. Experimental results show that, compared with the typical algorithms, the proposed algorithm not only can detect small target or area target under complex backgrounds, but also has excellent abilities of background suppression and target enhancement.

Detecting slowly moving infrared targets using temporal filtering and association strategy

The special characteristics of slowly moving infrared targets, such as containing only a few pixels, shapeless edge, low signal-to-clutter ratio, and low speed, make their detection rather difficult, especially when immersed in complex backgrounds. To cope with this problem, we propose an effective infrared target detection algorithm based on temporal target detection and association strategy. First, a temporal target detection model is developed to segment the interested targets. This model contains mainly three stages, i.e., temporal filtering, temporal target fusion, and cross-product filtering. Then a graph matching model is presented to associate the targets obtained at different times. The association relies on the motion characteristics and appearance of targets, and the association operation is performed many times to form continuous trajectories which can be used to help disambiguate targets from false alarms caused by random noise or clutter. Experimental results show that the proposed method can detect slowly moving infrared targets in complex backgrounds accurately and robustly, and has superior detection performance in comparison with several recent methods.

A novel infrared moving target detection algorithm based on multiscale codebook model

Traditional target detection algorithm based on codebook model only use pixel information of video image while spatial scale information of image is ignored, so the detection result usually has high false detection rate and the target’s characteristics is not obvious. To overcome this difficulty, a novel infrared (IR) moving target detection algorithm based on multiscale codebook model is presented in this paper. The main principle of this algorithm is to make full use of image pixel information and scale information for moving target detection. First, by Gauss pyramid image hierarchical model, the IR video is stratified into three layers, namely the original layer, the second layer and the top layer. Second, background codebook model is built for each layer image, the main feature information is discovered to update background codebook models, and then moving target in IR video is detected according to the updated background model. Finally, the fusion operation is done on detection results of three layers to get the final detection result. Compared with traditional detection algorithm based on codebook model, this IR target detection algorithm combines image pixel information and scale characteristics. By using this novel algorithm, the experiments on some real world IR images are performed. The whole algorithm implementing processes and results are analyzed, and this novel detection algorithm is evaluated from the two aspects: subjective evaluation and objective evaluation. From the experiment results, we can see that the proposed method has better detection effects, richer target information and lower false detection rate.

Scale-space point spread function based framework to boost infrared target detection algorithms

Small target detection is one of the major concern in the development of infrared surveillance systems. Detection algorithms based on Gaussian target modeling have attracted most attention from researchers in this field. However, the lack of accurate target modeling limits the performance of this type of infrared small target detection algorithms. In this paper, signal to clutter ratio (SCR) improvement mechanism based on the matched filter is described in detail and effect of Point Spread Function (PSF) on the intensity and spatial distribution of the target pixels is clarified comprehensively. In the following, a new parametric model for small infrared targets is developed based on the PSF of imaging system which can be considered as a matched filter. Based on this model, a new framework to boost model-based infrared target detection algorithms is presented. In order to show the performance of this new framework, the proposed model is adopted in Laplacian scale-space algorithms which is a well-known algorithm in the small infrared target detection field. Simulation results show that the proposed framework has better detection performance in comparison with the Gaussian one and improves the overall performance of IRST system. By analyzing the performance of the proposed algorithm based on this new framework in a quantitative manner, this new framework shows at least 20% improvement in the output SCR values in comparison with Laplacian of Gaussian (LoG) algorithm.

一种基于知识模型的红外目标检测方法

Based on analyzing the infrared characteristics and shape characteristics of condensing tower that has special construction rules,an infrared target detection algorithm based on knowledge model is proposed.Firstly,based on the infrared characteristics of condensing tower,the intensity,orientation and local entropy features are extracted to construct the visual attention model,which is used to extract salient regions as regions of interest in the infrared image.Secondly,based on the shape characteristics of condensing tower,hyperbola shape model is constructed for condensing tower,structure feature edges are extracted in the salient regions and used to fit the hyperbola shape model,and relevant decision rules are constructed to confirm the targets.The recall and precision of the experiment on a set of air-born infrared images can reach up to 98.67% and 93.67% respectively,which demonstrates the excellent performance of the proposed algorithm.Moreover,since the reference image is unnecessary in the proposed algorithm,the requirements for the data preparation is reduced greatly,which improve the practicality of the algorithm.

Small Target Detection Algorithm Based on Average Absolute Difference Maximum and Background Forecast

Detecting small targets in clutter scene and low SNR (Signal Noise Ratio) is an important and challenging problem in infrared (IR) images. In order to solve this problem, we should do works from two sides: enhancing targets and suppressing background. Firstly, in this paper, the system utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, it uses a predictor to suppress the background clutter. Finally, our approach extracts the interested small target with segment threshold. Experimental results show that the algorithm proposed has better performance with respect to probability of detection and less computation complexity. It is an effective small infrared target detection algorithm against complex background.