Small Target Detection Algorithm Research Articles

A More Efficient Algorithm for Small Target Detection in Unmanned Aerial Vehicles

Due to the relatively high shooting altitude of unmanned aerial vehicles (UAV), the captured images often contain a multitude of small‐scale targets. To solve the problems of small target scale, lack of semantic information, and high miss detection in drone target detection, in this paper we proposed a more effective unmanned aerial vehicle small target detection algorithm(MEU‐YOLOv5) based on YOLOv5s. Firstly, an efficient global contextual module is proposed to enhance the algorithm's performance in feature extraction while reducing the excessive loss of shallow features. Secondly, a small‐scale target detector is added to enhance the algorithm's detection capability for smaller targets. Lastly, a recursive multi‐level feature fusion path is introduced to better fuse the shallow and deep features of the images, reducing overfitting and improving the algorithm's generalizability and robustness. Experimental results demonstrated that compared to YOLOv5s, MEU‐YOLOv5 achieves a 7.4% improvement in mAP@0.5 and a 4.9% improvement in mAP@0.5:0.95. Additionally, the overall performance of this algorithm surpassed various algorithms in the YOLO series, including YOLOv3, YOLOv5l, YOLOv5m, and YOLOv8s. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Fine-Grained Feature Perception for Unmanned Aerial Vehicle Target Detection Algorithm

Unmanned aerial vehicle (UAV) aerial images often present challenges such as small target sizes, high target density, varied shooting angles, and dynamic poses. Existing target detection algorithms exhibit a noticeable performance decline when confronted with UAV aerial images compared to general scenes. This paper proposes an outstanding small target detection algorithm for UAVs, named Fine-Grained Feature Perception YOLOv8s-P2 (FGFP-YOLOv8s-P2), based on YOLOv8s-P2 architecture. We specialize in improving inspection accuracy while meeting real-time inspection requirements. First, we enhance the targets’ pixel information by utilizing slice-assisted training and inference techniques, thereby reducing missed detections. Then, we propose a feature extraction module with deformable convolutions. Decoupling the learning process of offset and modulation scalar enables better adaptation to variations in the size and shape of diverse targets. In addition, we introduce a large kernel spatial pyramid pooling module. By cascading convolutions, we leverage the advantages of large kernels to flexibly adjust the model’s attention to various regions of high-level feature maps, better adapting to complex visual scenes and circumventing the cost drawbacks associated with large kernels. To match the excellent real-time detection performance of the baseline model, we propose an improved Random FasterNet Block. This block introduces randomness during convolution and captures spatial features of non-linear transformation channels, enriching feature representations and enhancing model efficiency. Extensive experiments and comprehensive evaluations on the VisDrone2019 and DOTA-v1.0 datasets demonstrate the effectiveness of FGFP-YOLOv8s-P2. This achievement provides robust technical support for efficient small target detection by UAVs in complex scenarios.

Infrared small target detection algorithm based on filter kernel combination optimization learning method

Infrared small target detection in complex backgrounds remains a big challenge due to its small size and weak energy, which makes it easy to be submerged in complex backgrounds. Common filtering algorithms are difficult to apply to various complex scenes and generate many false alarms and poor robustness. Therefore, this paper proposes an infrared small target detection algorithm based on filter kernel combination optimization learning method. For the sample image block use typical filter kernels and weight coefficients to calculate the combination of filtering results, and construct the loss function of the results and the true value. The optimal weight coefficients are learned using an optimization method, which effectively achieves the fusion of multiple features, then we build a sample library containing comprehensive priori information. In the implementation, an online inference method based on similarity measure is proposed to match test images with sample library image blocks to obtain optimal combination weights. By utilizing the comprehensive priori information to enhance the adaptive ability of the algorithm for various complex backgrounds. Finally, get the position of the infrared small target. The experimental results show that our algorithm has excellent detection performance for infrared small targets with complex background and low signal to clutter ratio, and the AUC value is 0.9980, which is more robust than other comparative algorithms.

A small target detection algorithm based on improved YOLOv5 in aerial image.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network's focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.

Infrared small target detection based on variance difference weighted three-layer local contrast measure

Infrared (IR) small target detection technology is of paramount importance in the infrared search and track (IRST) system. The current methods typically exhibit strong performance in simple backgrounds, but they struggle to effectively suppress high-brightness background and strong noise. For the sake of diminish the false alarm rate of infrared small target detection algorithms under strong clutter interference, an infrared small target detection algorithm based on variance difference weighted three-layer local contrast measure (VDWTLCM) is proposed in this paper. The algorithm comprises two modules: the three-layer local contrast measure (TLCM) and the weighting function based on mean value of the variance difference (MVD). In the calculation module of TLCM, the image is processed pixel by pixel using a three-layer sliding window, while both the ratio form definition and the difference form definition are utilized to calculate the local contrast for each pixel, which can enhance the target and eliminate the high-brightness background. To calculate the MVD weighting function, a modified layered gradient kernel is first introduced to pre-process the IR image, aimed at achieving more effective clutter smoothing. And then the concept of variance difference is employed to further suppress the highlighted background edges. Ultimately, the target is extracted utilizing adaptive threshold segmentation. The experimental results demonstrate that, compared with the common infrared small target detection algorithms, the proposed algorithm improves the signal to clutter ratio gain (SCRG) by an average of 13.13 times and the background suppression factor (BSF) by an average of 10.02 times, and can achieve superior detection performance in different complex scenarios.

YOLOv8-MPEB small target detection algorithm based on UAV images

Target detection in Unmanned Aerial Vehicle (UAV) aerial images has gained significance within UAV application scenarios. However, UAV aerial images present challenges, including large-scale changes, small target sizes, complex scenes, and variable external factors, resulting in missed or false detections. This study proposes an algorithm for small target detection in UAV images based on an enhanced YOLOv8 model termed YOLOv8-MPEB. Firstly, the Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is substituted with the lightweight MobileNetV3 backbone network, consequently reducing model parameters and computational complexity, while also enhancing inference speed. Secondly, a dedicated small target detection layer is intricately designed to optimize feature extraction for multi-scale targets. Thirdly, the integration of the Efficient Multi-Scale Attention (EMA) mechanism within the Convolution to Feature (C2f) module aims to enhance the extraction of vital features and suppress superfluous ones. Lastly, the utilization of a bidirectional feature pyramid network (BiFPN) in the Neck segment serves to ameliorate detection errors stemming from scale variations and complex scenes, thereby augmenting model generalization. The study provides a thorough examination by conducting ablation experiments and comparing the results with alternative algorithms to substantiate the enhanced effectiveness of the proposed algorithm, with a particular focus on detection performance. The experimental outcomes illustrate that with a parameter count of 7.39 M and a model size of 14.5 MB, the algorithm attains a mean Average Precision (mAP) of 91.9 % on the custom-made helmet and reflective clothing dataset. In comparison to standard YOLOv8 models, this algorithm elevates average accuracy by 2.2 percentage points, reduces model parameters by 34 %, and diminishes model size by 32 %. It outperforms other prevalent detection algorithms in terms of accuracy and speed.

Underwater Rescue Target Detection Based on Acoustic Images.

In order to effectively respond to floods and water emergencies that result in the drowning of missing persons, timely and effective search and rescue is a very critical step in underwater rescue. Due to the complex underwater environment and low visibility, unmanned underwater vehicles (UUVs) with sonar are more efficient than traditional manual search and rescue methods to conduct active searches using deep learning algorithms. In this paper, we constructed a sound-based rescue target dataset that encompasses both the source and target domains using deep transfer learning techniques. For the underwater acoustic rescue target detection of small targets, which lack image feature accuracy, this paper proposes a two-branch convolution module and improves the YOLOv5s algorithm model to design an acoustic rescue small target detection algorithm model. For an underwater rescue target dataset based on acoustic images with a small sample acoustic dataset, a direct fine-tuning using optical image pre-training lacks cross-domain adaptability due to the different statistical properties of optical and acoustic images. This paper therefore proposes a heterogeneous information hierarchical migration learning method. For the false detection of acoustic rescue targets in a complex underwater background, the network layer is frozen during the hierarchical migration of heterogeneous information to improve the detection accuracy. In addition, in order to be more applicable to the embedded devices carried by underwater UAVs, an underwater acoustic rescue target detection algorithm based on ShuffleNetv2 is proposed to improve the two-branch convolutional module and the backbone network of YOLOv5s algorithm, and to create a lightweight model based on hierarchical migration of heterogeneous information. Through extensive comparative experiments conducted on various acoustic images, we have thoroughly validated the feasibility and effectiveness of our method. Our approach has demonstrated state-of-the-art performance in underwater search and rescue target detection tasks.

Improved YOLOv5s Algorithm for Small Target Detection in UAV Aerial Photography

Improved YOLOv5s Algorithm for Small Target Detection in UAV Aerial Photography

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

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.

Retracted: Small Target Detection Algorithm Based on Transfer Learning and Deep Separable Network

Retracted: Small Target Detection Algorithm Based on Transfer Learning and Deep Separable Network

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

Retracted: Robust Transmit Beamforming Algorithm for Low-Altitude Slow-Speed Small Target Detection.

[This retracts the article DOI: 10.1155/2022/6358133.].

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.