Small Target Detection Algorithm Research Articles

Improved YOLOv5s Algorithm for Small Target Detection in UAV Aerial Photography

Improved YOLOv5s Algorithm for Small Target Detection in UAV Aerial Photography

Graph-based Maximum Connected-component Learning Algorithm for Small Target Detection in Maritime Radars

Graph-based Maximum Connected-component Learning Algorithm for Small Target Detection in Maritime Radars

A New Algorithm for Small Target Detection From the Perspective of Unmanned Aerial Vehicles

A New Algorithm for Small Target Detection From the Perspective of Unmanned Aerial Vehicles

YOLOv8-FDF: A Small Target Detection Algorithm in Complex Scenes

YOLOv8-FDF: A Small Target Detection Algorithm in Complex Scenes

Small target detection algorithm based on multi-branch stacking and new sampling transition module.

Aiming at the problem that the SSD algorithm does not fully extract the feature information contained in each feature layer, as well as the feature information is easily lost during the sampling process, which makes the feature expression ineffective and leads to insufficient performance in small target detection. In this paper, AMT-SSD is proposed, a small target detection algorithm that incorporates the multi-branch stacking and new sampling transition module of the attention mechanism. In this algorithm, the composite attention mechanism is utilized to improve the correlation of features of the samples to be detected in terms of spatial and channels, and the efficiency of the algorithm; secondly, multi-branch stacking module is used to extract multi-size features for each feature layer, and different sizes of convolution kernels are utilized in parallel to fully extract their features and improve the expression of features; meanwhile, during the sampling process, the problem of missing features is solved by applying inverse subpixel convolution in the new sampling transition module. Experimentally, the AMT-SSD algorithm achieves 84.6% and 53.4% mAP metrics on the PASCAL VOC dataset and MS COCO dataset, respectively. This indicates that the AMT-SSD algorithm can effectively extract feature information that is beneficial to detection samples, and also performs well in reducing feature loss, which is effective for the algorithm to improve the algorithm on small targets.

DETR Novel Small Target Detection Algorithm Based on Swin Transformer

DETR Novel Small Target Detection Algorithm Based on Swin Transformer

LDHD‐Net: A Lightweight Network With Double Branch Head for Feature Enhancement of UAV Targets in Complex Scenes

The development of small UAV technology has led to the emergence of new challenges in UAV countermeasures. The timely detection of UAVs can effectively prevent potential infringements on airspace and privacy. Currently, methods based on deep learning demonstrate excellent performance in target detection. However, in complex scenes, there is a tendency for false alarms (FAs) and misdetections to occur at a higher rate. To solve these problems, we propose a lightweight infrared small target detection algorithm LDHD‐Net. First, we design a novel Ghost‐Shuffle module in the backbone network to enhance the network feature extraction capability. Meanwhile, we remove redundant layers from the network to make the backbone network more lightweight. Second, we design a hierarchical attention enhancement module in the neck network to improve the saliency of UAV targets and reduce background noise interference. In addition, we design a novel small target detection structure and prediction heads in the shallow layers of the network to improve small target detection accuracy. Finally, we design a novel attention dual‐branch head to reduce interference between different tasks and improve the real‐time performance of algorithm detection. The experimental results show that compared with the original model, inference time remains essentially the same, LDHD‐Net parameters are only 3.9 M and AP improves by 12.6%. Compared to SOTA methods, LDHD‐Net shows better performance on SIDD and Anti‐UAV410 datasets. The algorithm effectively improves the accuracy and real‐time detection of UAVs in complex scenes.

STD-YOLOv8: A lightweight small target detection algorithm for UAV perspectives

<p>When recognizing targets by unmanned aerial vehicles (UAVs), problems such as small size, dense dispersion, and complex background are likely to occur, resulting in low recognition rates. In order to solve the above problems, this work proposed a lightweight small target detection algorithm based on the YOLOv8n: STD-YOLOv8 algorithm. First, the regression problem of small targets in different training periods was optimized, the penalty term in the original loss was improved, and a new LIoU loss function was proposed, so that the size of the penalty term could be dynamically adjusted before and after training, thus improving the performance of the algorithm. Second, in order to better adapt to the small target scale and enhance the ability of small target feature acquisition, the SPD-Conv module was integrated in the backbone network, replacing the original stepwise convolutional layer and pooling layer, so as to solve the problems of loss of fine-grained information and low efficiency of feature representation existing in the current convolutional neural network (CNN) structure. In the neck part, nearest-neighbor upsampling was replaced by the feature reassembly assembly of features operator CARAFE (content-aware reassembly of features), which enabled the model to aggregate contextual information in a larger perceptual field and enhanced the feature representation in the neck. Finally, validation experiments were conducted by comparing different algorithms under the same VisDrone-2021 dataset. The results of the ablation experiments show that the algorithms proposed in this thesis have improved the recall (R), mAP50, and mAP95 by 4.7, 5.8 and 5.7%, respectively, compared with YOLOv8n. The results of the model generalization experiments on the TinyPerson dataset show that the algorithm in this paper has superior small target detection performance with only 1.2 M model parameters (1 M = 10<sup>6</sup>).</p>

Ghost-YOLO v8: An Attention-Guided Enhanced Small Target Detection Algorithm for Floating Litter on Water Surfaces

Ghost-YOLO v8: An Attention-Guided Enhanced Small Target Detection Algorithm for Floating Litter on Water Surfaces

DGW‐YOLOv8: A small insulator target detection algorithm based on deformable attention backbone and WIoU loss function

AbstractThe YOLO series of algorithms have made substantial contributions to the detection of insulator defects in power transmission line operations. However, existing target detection algorithms for the small target detection and low‐quality insulator images encounter difficulties in effectively capturing relevant features, resulting in a higher probability of target loss. To identify and classify defects in the operational state of insulators, an improved YOLOv8 target identification algorithm called DGW‐YOLOv8 is proposed in this paper. The deformable attention backbone of the DGW‐YOLOv8 target identification algorithm is designed by adding the deformable ConvNets v2 module and the global attention mechanism. This addition reduces the feature loss caused by the network feature processing, enhances the sensitivity of the algorithm to small‐scale targets, and reduces the impact caused by the different global positions of the targets. Additionally, to address the problem of low quality of captured images, WIoU v3 is used to replace CIoU in the original YOLOv8 target identification algorithm to optimize the loss function, reduce the degrees of freedom, and improve the network robustness. Experimental results demonstrate that the enhanced YOLOv8 algorithm can achieve an improvement of 2.4% and 5.5% in mAP and mAP50‐95, respectively, compared with the original algorithm.

A small object detection algorithm based on feature interaction and guided learning

At present, for the diverse target sizes, different angles, and complex environment in aerial images, how to detect small objects effectively in aerial images is still a challenge task. In order to further improve the detection accuracy and reduce the missing rate, in this paper, we proposes a small target detection algorithm based on feature interaction and guided learning. Firstly, the feature guided alignment module is designed to deal with the problem of information loss and overlap. Secondly, the misallocation of small object samples is optimized by introducing gaussian sample allocation strategy. Then the interactive parallel detection header is designed to solve the detection task conflict problem. Finally, the new regression loss function is designed to enhance the localization ability of the target. The experimental results on the Tinyperson dataset and Visdrone dataset show that this model can improve the detection accuracy and reduce the missing rate of small targets effectively.

Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation

The precise detection of military targets under complex conditions is a key factor to enhance the ability of war situation generation and prediction. The current technology can not overcome the problems of smoke and occlusion interference, target height change, and different scales in aerial video. In this paper, a multi feature cross fusion and cross layer cascade aerial military target detection algorithm (YOLOv5-MFLC) is proposed. Firstly, aiming at the high confidentiality of the military targets and the shortage of battlefield aerial image resources, a real scene based aerial military target dataset is constructed, and the methods of random splicing and random extraction embedding are used for data enhancement in order to improve the diversity and generalization of targets. Secondly, aiming at the problem of complex background interference, a multi feature cross fusion attention mechanism is constructed to enhance the available information of target features. Finally, for the multi-scale problem of targets in aerial images, a cross layer cascaded multi-scale feature fusion pyramid is designed to improve the detection accuracy of cross scale targets. The experimental results show that, comparing with the existing advanced detection models, the detection accuracy of the algorithm in this paper has been greatly improved. The average accuracy of the algorithm can reach 81.0%, which is 5.2% higher than the original network. In particular, it has reached 55.9% in the smaller target category "person", which is 9.4% higher. And the experimental results further show the usefulness of the improved algorithm for small target detection. At the same time, the detection rate of this algorithm can reach 56 frame/s, which can effectively achieve accurate and fast detection of battlefield targets, and has certain experience value for guiding complex modern wars.

STF-YOLO: A small target detection algorithm for UAV remote sensing images based on improved SwinTransformer and class weighted classification decoupling head

Due to the high altitude, high imaging resolution, complex background, and tilted shooting angle of the UAV images, detecting small targets poses challenges. In recent years, numerous deep learning-based methods have achieved good results in feature extraction and localization classification, but they fail to meet the needs of faster and more accurate recognition of small objects. This paper proposes a novel UAV remote sensing image small target detection algorithm called STF-YOLO to address the aforementioned issues. Firstly, we introduce the innovative SwinTransformer, a popular technique in the field of NLP, and combine it with CNNs to propose a novel convolutional structure called STRCN. Furthermore, this paper presents a novel lightweight classifier called CNeB, which is innovatively designed and incorporated into the Backbone part. CNeB exhibits advantages such as high recognition accuracy and parameter efficiency. During the process, we further enhance the prediction accuracy by incorporating a parameter-free attention mechanism called SimAM to improve the overall precision of the model. Moreover, we propose a novel detection head called CWDHead, which strengthens the weighting of classification capabilities and significantly improves the recognition accuracy of similar inter-class and intra-class instances. Lastly, we design a novel data augmentation method called SVM, which enriches the training dataset and enhances the robustness and generalization ability of the model. The results indicate that by applying enhancements such as WBF and TTA, our proposed algorithm, STF-YOLO, exhibits significant improvements over current state-of-the-art methods on the publicly available VisDrone dataset. It demonstrates noticeable advancements in widely recognized metrics such as 3.9%mAP, and 2.0%AP50.

Small Target Detection Algorithm Based on Wavelet Domain Features and Weighted Sample Allocation Strategy

Small target detection using UAV aerial photography has emerged as a popular research topic. The resolution of small target images is low and the background is complex. In this paper, utilizing a single-stage target detection algorithm ATSS as its foundation, we propose a small target network detection model SDW-Net that combines deformation convolution with wavelet domain feature enhancement by weighting the samples from both classification and regression tasks, and combining the strong local feature representation ability of discrete wavelet transform. Firstly, the original features are obtained by using discrete wavelet transform, and the weighted wavelet features are obtained through the deformable convolution block and the spatial channel attention unit; secondly, the weighted features of the regular domain are obtained through the inverse wavelet trans-form; finally, the original feature and the wavelet features are fused to enrich the details of the small target image and sent to the detection head for output. According to the experimental findings, when compared to the ATSS algorithm, the proposed detection model achieved an average increase in prediction accuracy of 7.2%, and a recall rate increase of 3.7%, and the problem of missing detection and false detection can be alleviated, which can provide a new technical solution for small-sized target detection scenarios.

Small target detection algorithm for printing defects detection based on context structure perception and multi-scale feature fusion

Small target detection algorithm for printing defects detection based on context structure perception and multi-scale feature fusion

UAV small target detection algorithm based on an improved YOLOv5s model

The targets of UAV target detection are usually small targets, and the backgrounds are complex. In this work, aiming at the problem that small targets are easy to be missed or misdetected during the UAV detection, an improved YOLOv5s_MSES target detection algorithm based on YOLOv5s is proposed. First of all, to solve the problem of UAV’s difficulty in detecting small targets, the detection layer is ameliorated into the small target detection layer STD, which makes the model more easily detect the small targets. Then, the multi-scale feature fusion module is added to improve the detection accuracy of the small targets. Furthermore, by combining multi-scale module and attention module, a new connection method is proposed to retain the large scale of feature information. Finally, in contrast with some existent methods, the experimental results of VisDrone2019 UAV target detection dataset show that our proposed YOLOv5s_MSES can achieve the better detection effect, and more effectively complete the small target detection task for UAV aerial photography images.

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.

Infrared Small Target Detection Based on a Temporally-Aware Fully Convolutional Neural Network

In the field of computer vision, the detection of infrared small targets (IRSTD) is a crucial research area that plays an important role in space exploration, infrared warning systems, and other applications. However, the existing IRSTD methods are prone to generating a higher number of false alarms and an inability to accurately locate the target, especially in scenarios with low signal-to-noise ratio or high noise interference. To address this issue, we proposes a fully convolutional-based small target detection algorithm (FCST). The algorithm builds on the anchor-free detection method FCOS and adds a focus structure and a single aggregation approach to design a lightweight feature extraction network that efficiently extracts features for small targets. Furthermore, we propose a feature refinement mechanism to emphasize the target and suppress conflicting information at multiple scales, enhancing the detection of infrared small targets. Experimental results demonstrate that the proposed algorithm achieves a detection rate of 95% and a false alarm rate of 2.32% for IRSTD tasks. To tackle even more complex scenarios, we propose a temporally-aware fully convolutional infrared small target detection (TFCST) algorithm that leverages both spatial and temporal information from sequence images. Building on a single-frame detection network, the algorithm incorporates ConvLSTM units to extract spatiotemporal contextual information from the sequence images, boosting the detection of infrared small targets. The proposed algorithm shows fast detection speed and achieves a 2.73% improvement in detection rate and an 8.13% reduction in false alarm rate relative to the baseline single-frame detection networks.

Research on real-time dense small target detection algorithm of UAV based on YOLOv3-SPP

Research on real-time dense small target detection algorithm of UAV based on YOLOv3-SPP

RetinaNet-vline: a flexible small target detection algorithm for efficient aggregation of information

RetinaNet-vline: a flexible small target detection algorithm for efficient aggregation of information