Small Target Detection Method Research Articles

Watermarking Algorithm for Remote Sensing Images based on Ring-Shaped Template Watermark and Multiscale LCM

Identifying template watermarks under severe geometric distortions is a significant scientific problem in the current watermarking research for remote sensing images. We propose a novel watermarking algorithm that integrates the ring-shaped template watermark with the multiscale local contrast measure (LCM) method. In the embedding stage, the ring-shaped template watermark is embedded into the discrete Fourier transform (DFT) magnitude coefficients, converting the watermark into small targets in the DFT domain. During the detection stage, the multiscale LCM, a classic infrared small target detection method, enhances these small targets and generates a contrast map. Peak detection is then performed on the contrast map to determine the radius of the template watermark. Finally, circular edge local binarization is applied to extract the watermark information. The proposed method enables synchronization recovery of watermarks under blind conditions. The experimental results demonstrate that the method possesses strong robustness against various geometric attacks such as rotation, scaling, translation, and cropping. It outperforms comparative algorithms in terms of robustness and also exhibits good imperceptibility.

A Spatial Small Target Detection Method Based on a Multi-Scale Feature Fusion Pyramid Spatial Small Target Detection Method Based on Multi-scale Feature Fusion Pyramid

Small target detection has become an important part of space exploration missions. The existence of weak illumination and interference from the background of star charts in deep and distant space has brought great challenges to space target detection. In addition, the distance of space targets is usually far, so most of them are small targets in the image, and the detection of small targets is also very difficult. To solve the above problems, we propose a multi-scale feature fusion pyramid network. First, we propose the CST module of a CNN fused with Swin Transformer as the feature extraction module of the feature pyramid network to enhance the extraction of target features. Then, we improve the SE attention mechanism and construct the CSE module to find the attention region in the dense star map background. Finally, we introduce improved spatial pyramid pooling to fuse more features to increase the sensory field to obtain multi-scale object information and improve detection performance for small targets. We provide two versions and conducted a detailed ablation study to empirically validate the effectiveness and efficiency of the design of each component in our network architecture. The experimental results show that our network improved in performance compared to the existing feature pyramid.

Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection

Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.

Detection of Small Targets in Photovoltaic Cell Defect Polarization Imaging Based on Improved YOLOv7

A photovoltaic cell defect polarization imaging small target detection method based on improved YOLOv7 is proposed to address the problem of low detection accuracy caused by insufficient feature extraction ability in the process of small target defect detection. Firstly, polarization imaging technology is introduced, using polarization degree images as inputs to enhance the edge contour information of YOLOv7 for detecting small targets; then, the COT self-attention mechanism is added to reconstruct the SPPCSPC module to improve YOLOv7’s ability to capture and fuse small target features in complex backgrounds; next, the normalized Wasserstein distance (NWD) is used to replace the traditional loss function based on intersection over union (IoU) metric, reducing the boundary offset between the prior box and the closest real target box in the prediction process of the object detection model and reducing the sensitivity of the YOLOv7 network to small object position deviations; finally, by constructing a shortwave infrared polarization imaging system to obtain polarization images of photovoltaic cells and detect small targets with scratch defects in photovoltaic cells, the applicability and effectiveness of the proposed method are verified. The results show that the proposed method has good recognition ability for small target defects in photovoltaic cells. By applying the constructed dataset, the detection accuracy reaches 98.08%, the recall rate reaches 95.06% and the mAP reaches 98.83%.

A single-frame infrared small target detection method based on joint feature guidance

Single-frame infrared small target detection is affected by the low image resolution and small target size, and is prone to the problems of small target feature loss and positional offset during continuous downsampling; at the same time, the sparse features of the small targets do not correlate well with the global-local linkage of the background features. To solve the above problems, this paper proposes an efficient infrared small target detection method. First, this paper incorporates BlurPool in the feature extraction part, which reduces the loss and positional offset of small target features in the process of convolution and pooling. Second, this paper designs an interactive attention deep feature fusion module, which acquires the correlation information between the target and the background from a global perspective, and designs a compression mechanism based on deep a priori knowledge, which reduces the computational difficulty of the self-attention mechanism. Then, this paper designs the context local feature enhancement and fusion module, which uses deep semantic features to dynamically guide shallow local features to realize enhancement and fusion. Finally, this paper proposes an edge feature extraction module for shallow features, which utilizes the complete texture and location information in the shallow features to assist the network to initially locate the target position and edge shape. Numerous experiments show that the method in this paper significantly improves nIoU, F1-Measure and AUC on IRSTD-1k Datasets and NUAA-SIRST Datasets.

RETRACTED: Chen et al. An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring. Remote Sens. 2023, 15, 2922

The journal retracts the article titled “An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring” [...]

Infrared Maritime Small-Target Detection Based on Fusion Gray Gradient Clutter Suppression

The long-distance ship target turns into a small spot in an infrared image, which has the characteristics of small size, weak intensity, limited texture information, and is easily affected by noise. Moreover, the presence of heavy sea clutter, including sun glints that exhibit local contrast similar to small targets, negatively impacts the performance of small-target detection methods. To address these challenges, we propose an effective detection scheme called fusion gray gradient clutter suppression (FGGCS), which leverages the disparities in grayscale and gradient between the target and its surrounding background. Firstly, we designed a harmonic contrast map (HCM) by using the two-dimensional difference of Gaussian (2D-DoG) filter and eigenvalue harmonic mean of the structure tensor to highlight high-contrast regions of interest. Secondly, a local gradient difference measure (LGDM) is designed to distinguish isotropic small targets from background edges with local gradients in a specific direction. Subsequently, by integrating the HCM and LGDM, we designed a fusion gray gradient clutter suppression map (FGGCSM) to effectively enhance the target and suppress clutter from the sea background. Finally, an adaptive constant false alarm threshold is adopted to extract the targets. Extensive experiments on five real infrared maritime image sequences full of sea glints, including a small target and sea–sky background, show that FGGCS effectively increases the signal-to-clutter ratio gain (SCRG) and the background suppression factor (BSF) by more than 22% and 82%, respectively. Furthermore, its receiver operating characteristic (ROC) curve has an obviously more rapid convergence rate than those of other typical detection algorithms and improves the accuracy of small-target detection in complex maritime backgrounds.

Dim and Small Target Detection Method via Gradient Features Guided Local Contrast

Dim and Small Target Detection Method via Gradient Features Guided Local Contrast

4DST-BTMD: An Infrared Small Target Detection Method Based on 4-D Data-Sphered Space

4DST-BTMD: An Infrared Small Target Detection Method Based on 4-D Data-Sphered Space

Infrared Small Target Detection Method Based on High-Low Frequency Semantic Reconstruction

Infrared Small Target Detection Method Based on High-Low Frequency Semantic Reconstruction

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion.

As affected by limited information and the complex background, the accuracy of small-target water-floating garbage detection is low. To increase the detection accuracy, in this research, a small-target detection method based on APM-YOLOv7 (the improved YOLOv7 with ACanny PConv-ELAN and MGA attention) is proposed. Firstly, the adaptive algorithm ACanny (adaptive Canny) for river channel outline extraction is proposed to extract the river channel information from the complex background, mitigating interference of the complex background and more accurately extracting the features of small-target water-floating garbage. Secondly, the lightweight partial convolution (PConv) is introduced, and the partial convolution-efficient layer aggregation network module (PConv-ELAN) is designed in the YOLOv7 network to improve the feature extraction capability of the model from morphologically variable water-floating garbage. Finally, after analyzing the limitations of the YOLOv7 network in small-target detection, a multi-scale gated attention for adaptive weight allocation (MGA) is put forward, which highlights features of small-target garbage and decreases missed detection probability. The experimental results showed that compared with the benchmark YOLOv7, the detection accuracy in the form of the mean Average Precision (mAP) of APM-YOLOv7 was improved by 7.02%, that of mmAP (mAP0.5:0.95) was improved by 3.91%, and Recall was improved by 11.82%, all of which meet the requirements of high-precision and real-time water-floating garbage detection and provide reliable reference for the intelligent management of water-floating garbage.

YWnet: A convolutional block attention-based fusion deep learning method for complex underwater small target detection

Underwater target detection holds a noteworthy role in the field of marine exploration. However, it is difficult to extract useful feature information from blurred images with complex backgrounds, resulting in suboptimal and unsatisfactory target detection in conventional models. Among them, YOLOv5 leverages the advantages of fast detection performs better in detecting underwater samples. Nevertheless, YOLOv5 still faces difficulties including missed and incorrect detections due to the underwater environment's small scale of objects, dense distribution of organisms, and occlusion. To address these challenges, we propose a novel YoLoWaternet (YWnet) model that builds upon the YOLOv5 framework for complex underwater species detection with three main innovations: 1) A convolutional block attention module (CBAM) is introduced to enhance feature extraction for blurry images in the initial stages of the network and a new feature fusion network called the CRFPN is created to transfer important information and detect underwater targets. 2) A novel feature extraction module is presented, namely, the skip residual C3 module (SRC3), by effectively merging information from various scales to minimize the loss of original data during transmission. 3) Regression and classification algorithms are separated using the decoupled head to improve the effectiveness of detection and the EIoU loss function is employed to accelerate the convergence speed. Finally, the experimental results demonstrate that YWnet achieves remarkable accuracies of 73.2% mAp and 39.3% mAp50–95 on the underwater dataset, surpassing YOLOv5 by 2.3% and 2.4%, respectively. Furthermore, the proposed fusion model outperforms nine state-of-the-art baseline models on the undersea dataset and has generalization capabilities in other datasets.

MGAF‐net: Gaussian saliency features guided infrared small target detection network

AbstractRobust infrared dim small target detection under sparse features is a difficult problem in the field of small target detection. Therefore, in order to solve this problem, this paper proposes an infrared dim small target detection network based on multi‐scale Gaussian significance and attention feature fusion (MGAF)‐net. In MGAF‐net, MGAF module is proposed to extract multi‐scale Gaussian saliency features of small targets. At the same time, in order to better fuse the multi‐scale Gaussian saliency features of the target, the attention mechanism is introduced in the MGAF module to enrich the feature expression of the target. The experimental results show that the proposed detection method has higher detection accuracy and higher detection speed than the existing advanced infrared dim small target detection methods.

CCRANet: A Two-Stage Local Attention Network for Single-Frame Low-Resolution Infrared Small Target Detection

Infrared small target detection technology is widely used in infrared search and tracking, infrared precision guidance, low and slow small aircraft detection, and other projects. Its detection ability is very important in terms of finding unknown targets as early as possible, warning in time, and allowing for enough response time for the security system. This paper combines the target characteristics of low-resolution infrared small target images and studies the infrared small target detection method under a complex background based on the attention mechanism. The main contents of this paper are as follows: (1) by sorting through and expanding the existing datasets, we construct a single-frame low-resolution infrared small target (SLR-IRST) dataset and evaluate the existing datasets on three aspects—target number, target category, and target size; (2) to improve the pixel-level metrics of low-resolution infrared small target detection, we propose a small target detection network with two stages and a corresponding method. Regarding the SLR-IRST dataset, the proposed method is superior to the existing methods in terms of pixel-level metrics and target-level metrics and has certain advantages in model processing speed.

Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration

Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the time-consuming nature of solving the model. To tackle these two challenges, we propose a novel infrared small-target detection method using a Background-Suppression Proximal Gradient (BSPG) and GPU parallelism. We first propose a new continuation strategy to suppress the strong edges. This strategy enables the model to simultaneously consider heterogeneous components while dealing with low-rank backgrounds. Then, the Approximate Partial Singular Value Decomposition (APSVD) is presented to accelerate solution of the LRSD problem and further improve the solution accuracy. Finally, we implement our method on GPU using multi-threaded parallelism, in order to further enhance the computational efficiency of the model. The experimental results demonstrate that our method out-performs existing advanced methods, in terms of detection accuracy and execution time.

Deep Learning-Based Small Target Detection for Satellite–Ground Free Space Optical Communications

Free space optical (FSO) channels between a low earth orbit (LEO) satellite and a ground station (GS) use a highly directional optical beam that necessitates a continuous line-of-sight (LOS) connection. In this paper, we propose a deep neural network (DNN)-based small target detection method that detects the position of a LEO satellite in an infrared image, which can be used to determine the receiver alignment for establishing the LOS link. For the infrared small target detection task without excessive down-sampling, we design a target detection model using a modified ResNest-based feature extraction network (FEN), a custom feature pyramid network (FPN), and a target determination network (TDN). ResNest utilizes the feature map attention mechanism and multi-path propagation necessary for robust feature extraction of small infrared targets. The custom FPN combines multi-scale feature maps generated from the modified ResNest to obtain robust semantics across all scales. Finally, the semantically strong multi-scale feature maps are fed into the TDN to detect small infrared targets and determine their location in infrared images. Experimental results using two widely used point spread functions (PSFs) demonstrate that the proposed algorithm outperforms the conventional schemes and detects small targets with a true detection rate of 99.4% and 94.0%.

Improved Method for Apple Fruit Target Detection Based on YOLOv5s

Images captured using unmanned aerial vehicles (UAVs) often exhibit dense target distribution and indistinct features, which leads to the issues of missed detection and false detection in target detection tasks. To address these problems, an improved method for small target detection called YOLOv5s is proposed to enhance the detection accuracy for small targets such as apple fruits. By applying improvements to the RFA module, DFP module, and Soft-NMS algorithm, as well as integrating these three modules together, accurate detection of small targets in images can be achieved. Experimental results demonstrate that the integrated, improved model achieved a significant improvement in detection accuracy, with precision, recall, and mAP increasing by 3.6%, 6.8%, and 6.1%, respectively. Furthermore, the improved method shows a faster convergence speed and lower loss value during the training process, resulting in higher recognition accuracy. The results of this study indicate that the proposed improved method exhibits a good performance in apple fruit detection tasks involving UAV imagery, which is of great significance for fruit yield estimation. The research findings demonstrate the effectiveness and feasibility of the improved method in addressing small target detection tasks, such as apple fruit detection.

An Infrared Small Target Detection Method Based on Attention Mechanism.

The human visual attention system plays an important role in infrared target recognition because it can quickly and accurately recognize infrared small targets and has good scene adaptability. This paper proposes an infrared small target detection method based on an attention mechanism, which consists of three modules: a bottom-up passive attention module, a top-down active attention module, and decision feedback equalization. In the top-down active attention module, given the Gaussian characteristics of infrared small targets, the idea of combining knowledge-experience Gaussian shape features is applied to implement feature extraction, and quaternion cosine transform is performed to achieve multi-dimensional fusion of Gaussian shape features, thereby achieving complementary fusion of multi-dimensional feature information. In the bottom-up passive attention module, considering that the difference in contrast and motion between the target and the background can attract attention easily, an optimal fast local contrast algorithm and improved circular pipeline filtering are adopted to find candidate target regions. Meanwhile, the multi-scale Laplacian of the Gaussian filter is adopted to estimate the optimal size of the infrared small target. The fast local contrast algorithm based on box filter acceleration and structure optimization is employed to extract local contrast features, and candidate target regions can be obtained by using an adaptive threshold. Besides, the mean gray, target size, Gaussian consistency, and circular region constraint are used in pipeline filtering to extract motion regions, and the false-alarm rate is reduced effectively. Finally, decision feedback equalization is adopted to obtain real targets. Experiments are conducted on some real infrared images involving complex backgrounds with sea, sky, and ground clutters, and the experimental results indicate that the proposed method can achieve better detection performance than conventional baseline methods, such as RLCM, ILCM, PQFT, MPCM, and ADMD. Also, mathematical proofs are provided to validate the proposed method.

Infrared small target detection based on multi-perception of target features

The rapid and efficient detection of infrared dim and small targets in complex backgrounds is a critical area of research in infrared image processing. When confronted with limited target features, it becomes vital to mine and construct additional features in order to accurately differentiate the target from the surrounding background and noise. This paper presents a novel small infrared target detection method based on multi-perception of target features which involves four main stages. First, two new target features are constructed to reduce the similarity between the target and noise and smooth the background. Second, two designed filters are applied to enhance the significance of the target and achieve preliminary detection. Third, structure tensor analysis is used to remove the significant background edge regions and perceive targets with strong corner characteristics. Finally, pseudo targets are eliminated by utilizing target candidate areas to achieve the final detection. Experimental results demonstrate that this method is faster and more robust compared to existing detection algorithms, with superior adaptability and detection performance.

Infrared Dim and Small Target Sequence Dataset Generation Method Based on Generative Adversarial Networks

With the development of infrared technology, infrared dim and small target detection plays a vital role in precision guidance applications. To address the problems of insufficient dataset coverage and huge actual shooting costs in infrared dim and small target detection methods, this paper proposes a method for generating infrared dim and small target sequence datasets based on generative adversarial networks (GANs). Specifically, first, the improved deep convolutional generative adversarial network (DCGAN) model is used to generate clear images of the infrared sky background. Then, target–background sequence images are constructed using multi-scale feature extraction and improved conditional generative adversarial networks. This method fully considers the infrared characteristics of the target and the background, which can achieve effective expansion of the image data and provide a test set for the infrared small target detection and recognition algorithm. In addition, the classifier’s performance can be improved by expanding the training set, which enhances the accuracy and effect of infrared dim and small target detection based on deep learning. After experimental evaluation, the dataset generated by this method is similar to the real infrared dataset, and the model detection accuracy can be improved after training with the latest deep learning model.