Small Target Detection Research Articles

TMP: Temporal Motion Perception with spatial auxiliary enhancement for moving Infrared dim-small target detection

Infrared dim and small target detection has significant research value and application prospects in both military affairs and civil safety scenes. This topic presents substantial challenges due to the small size of the targets and their low contrast against the background in images. In the past ten years, most detection methodologies have primarily focused on single-frame images, mainly relying on spatial-domain image features to identify dim and small targets, with limited consideration of temporal-domain motion features. However, unlike general objects, the image features of dim-small targets are often easy to be interfered by background, and even seriously lost in capturing stages. These deficiencies can be the primary factors to impact the detection performance of single-frame methods. To promote infrared dim-small target detection, this paper proposes a novel multi-frame pipeline called Temporal Motion Perception with spatial auxiliary enhancement (TMP), which consists of two parallel feature extraction branches. One auxiliary branch is designed to capture traditional image features from spatial domain, and the other is proposed to extract the motion features of targets from temporal domain. In addition, a new Complementary Symmetry Weighting module is specially designed to fulfill the cross-domain fusion of spatio-temporal features, further enhancing feature representation. Experimental results on three public datasets demonstrate the efficiency of our proposed scheme. Compared with existing methods, it could often refresh the state-of-the-art (SOTA) performance on most metrics. Our codes are available at https://github.com/UESTC-nnLab/TMP.

An Improved YOLOv8n Used for Fish Detection in Natural Water Environments.

To improve detection efficiency and reduce cost consumption in fishery surveys, target detection methods based on computer vision have become a new method for fishery resource surveys. However, the specialty and complexity of underwater photography result in low detection accuracy, limiting its use in fishery resource surveys. To solve these problems, this study proposed an accurate method named BSSFISH-YOLOv8 for fish detection in natural underwater environments. First, replacing the original convolutional module with the SPD-Conv module allows the model to lose less fine-grained information. Next, the backbone network is supplemented with a dynamic sparse attention technique, BiFormer, which enhances the model's attention to crucial information in the input features while also optimizing detection efficiency. Finally, adding a 160 × 160 small target detection layer (STDL) improves sensitivity for smaller targets. The model scored 88.3% and 58.3% in the two indicators of mAP@50 and mAP@50:95, respectively, which is 2.0% and 3.3% higher than the YOLOv8n model. The results of this research can be applied to fishery resource surveys, reducing measurement costs, improving detection efficiency, and bringing environmental and economic benefits.

Feature-Enhanced Attention and Dual-GELAN Net (FEADG-Net) for UAV Infrared Small Object Detection in Traffic Surveillance

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

MWIRGas-YOLO: Gas Leakage Detection Based on Mid-Wave Infrared Imaging.

The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection.

Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field.

Due to the existence of cotton weeds in a complex cotton field environment with many different species, dense distribution, partial occlusion, and small target phenomena, the use of the YOLO algorithm is prone to problems such as low detection accuracy, serious misdetection, etc. In this study, we propose a YOLOv8-DMAS model for the detection of cotton weeds in complex environments based on the YOLOv8 detection algorithm. To enhance the ability of the model to capture multi-scale features of different weeds, all the BottleNeck are replaced by the Dilation-wise Residual Module (DWR) in the C2f network, and the Multi-Scale module (MSBlock) is added in the last layer of the backbone. Additionally, a small-target detection layer is added to the head structure to avoid the omission of small-target weed detection, and the Adaptively Spatial Feature Fusion mechanism (ASFF) is used to improve the detection head to solve the spatial inconsistency problem of feature fusion. Finally, the original Non-maximum suppression (NMS) method is replaced with SoftNMS to improve the accuracy under dense weed detection. In comparison to YOLO v8s, the experimental results show that the improved YOLOv8-DMAS improves accuracy, recall, mAP0.5, and mAP0.5:0.95 by 1.7%, 3.8%, 2.1%, and 3.7%, respectively. Furthermore, compared to the mature target detection algorithms YOLOv5s, YOLOv7, and SSD, it improves 4.8%, 4.5%, and 5.9% on mAP0.5:0.95, respectively. The results show that the improved model could accurately detect cotton weeds in complex field environments in real time and provide technical support for intelligent weeding research.

Detection Based on Semantics and a Detail Infusion Feature Pyramid Network and a Coordinate Adaptive Spatial Feature Fusion Mechanism Remote Sensing Small Object Detector

In response to the challenges of remote sensing imagery, such as unmanned aerial vehicle (UAV) aerial imagery, including differences in target dimensions, the dominance of small targets, and dense clutter and occlusion in complex environments, this paper optimizes the YOLOv8n model and proposes an innovative small-object-detection model called DDSC-YOLO. First, a DualC2f structure is introduced to improve the feature-extraction capabilities of the model. This structure uses dual-convolutions and group convolution techniques to effectively address the issues of cross-channel communication and preserving information in the original input feature mappings. Next, a new attention mechanism, DCNv3LKA, was developed. This mechanism uses adaptive and fine-grained information-extraction methods to simulate receptive fields similar to self-attention, allowing adaptation to a wide range of target size variations. To address the problem of false and missed detection of small targets in aerial photography, we designed a Semantics and Detail Infusion Feature Pyramid Network (SDI-FPN) and added a dedicated detection scale specifically for small targets, effectively mitigating the loss of contextual information in the model. In addition, the coordinate adaptive spatial feature fusion (CASFF) mechanism is used to optimize the original detection head, effectively overcoming multi-scale information conflicts while significantly improving small target localization accuracy and long-range dependency perception. Testing on the VisDrone2019 dataset shows that the DDSC-YOLO model improves the mAP0.5 by 9.3% over YOLOv8n, and its performance on the SSDD and RSOD datasets also confirms its superior generalization capabilities. These results confirm the effectiveness and significant progress of our novel approach to small target detection.

Enhancing YOLOv5 for superior PCB defect detection: lightweight heads and small target precision

Abstract This paper introduces an improved YOLOv5 network designed to achieve enhanced performance in the field of printed circuit board (PCB) defect detection. We incorporate a lightweight detection head, reducing the number of network parameters and improving computational efficiency. This not only facilitates operation in resource-constrained environments but also maintains detection accuracy. To better address the specific requirements of PCB defect detection, we introduce a small target detection head. By adding a detection head specifically designed for small targets, we enhance the network’s ability to perceive subtle defects, thereby improving detection accuracy. Finally, we validate our proposed improvements through experiments on a large-scale PCB defect dataset. Compared to the traditional YOLOv5 network, our model exhibits significant improvements in both accuracy and efficiency.

An Improved U-Net Infrared Small Target Detection Algorithm Based on Multi-Scale Feature Decomposition and Fusion and Attention Mechanism.

Infrared small target detection technology plays a crucial role in various fields such as military reconnaissance, power patrol, medical diagnosis, and security. The advancement of deep learning has led to the success of convolutional neural networks in target segmentation. However, due to challenges like small target scales, weak signals, and strong background interference in infrared images, convolutional neural networks often face issues like leakage and misdetection in small target segmentation tasks. To address this, an enhanced U-Net method called MST-UNet is proposed, the method combines multi-scale feature decomposition and fusion and attention mechanisms. The method involves using Haar wavelet transform instead of maximum pooling for downsampling in the encoder to minimize feature loss and enhance feature utilization. Additionally, a multi-scale residual unit is introduced to extract contextual information at different scales, improving sensory field and feature expression. The inclusion of a triple attention mechanism in the encoder structure further enhances multidimensional information utilization and feature recovery by the decoder. Experimental analysis on the NUDT-SIRST dataset demonstrates that the proposed method significantly improves target contour accuracy and segmentation precision, achieving IoU and nIoU values of 80.09% and 80.19%, respectively.

A new network model for multiple object detection for autonomous vehicle detection in mining environment

AbstractConsidering the challenges of low multi‐object detection accuracy and difficulty in identifying small targets caused by challenging environmental conditions including irregular lighting patterns and ambient noise levels in the mining environment with autonomous electric locomotives. A new network model based on SOD−YOLOv5s−4L has been proposed to detect multi‐objects for autonomous electric locomotives in underground coal mines. Improvements have been applied in YOLOv5s to construct the SOD−YOLOv5s−4L model, by introducing the SIoU loss function to address the mismatch between real and predicted bounding box directions, facilitating the model to learn target position information more efficiently. This research introduces a decoupled head to enhance feature fusion and improve the positioning precision of the network model, enabling rapid capture of multi‐scale target features. Furthermore, the detection capability of the model has been increased by introducing the small target detection layer which is developed by increasing the number of detection layers from three to four. The experimental results on multiple object detection dataset show that the proposed model achieves significant improvement in mean average precision (mAP) of almost 98% for various types of targets and an average precision (AP) of nearly 99% for small targets on the other hand it achieves 5.19% (mAP) and 9.79% (AP) compared to the YOLOv5s model. Furthermore, comparative analysis with other models like YOLOv7 and YOLOv8 shows that the proposed model has superior performance in terms of object detection.

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.

Improved YOLOv8 algorithms for small object detection in aerial imagery

In drone aerial target detection tasks, a high proportion of small targets and complex backgrounds often lead to false positives and missed detections, resulting in low detection accuracy. To improve the accuracy of the detection of small targets, this study proposes two improved models based on YOLOv8s, named IMCMD_YOLOv8_small and IMCMD_YOLOv8_large. Each model accommodates different application scenarios. First, the network structure was optimized by removing the backbone P5 layer used to detect large targets and merging the P4, P3, and P2 layers, which are better suited for detecting medium and small targets; P3 and P2 serve as detection heads to focus more on small targets. Subsequently, the coordinate attention mechanism is integrated into the backbone’s C2f, to create a C2f_CA module that enhances the model’ s focus on key information and secures a richer flow of gradient information. Subsequently, a multiscale attention feature fusion module was designed to merge the shallow and deep features. Finally, a Dynamic Head was introduced to unify the perception of scale, space, and tasks, further enhancing the detection capability for small targets. Experimental results on the VisDrone2019 dataset demonstrated that, compared with YOLOv8s, IMCMD_YOLOv8_small achieved improvements of 7.7% and 5.1% in mAP@0.5 and mAP@0.5:0.95, respectively, with a 73.0% reduction in the parameter count. The IMCMD_YOLOv8_large model showed even more significant improvements in these metrics, reaching 10.8% and 7.3%, respectively, with a 47.7% reduction in the parameter count, displaying superior performance in small target detection tasks. The improved models not only enhanced the detection accuracy but also achieved model lightweighting, thereby proving the effectiveness of the improvement strategies and showcasing superior performance compared with other classic models.

Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting

To address the technical challenge of identifying tiny defects, especially dust and point defects, on mobile phone flat glass, an automatic optical inspection system is established. The system investigates algorithms including imaging principles, target detection models, data augmentation, foreground segmentation, and image fusion. The system builds an automatic optical inspection platform to collect glass defect samples. It illuminates the glass samples with a combined total reflection–grazing light source, collects the defect sample data, segments the background and defects of the collected data, generates the defect mask, and extracts the complete defects of the cell phone flat glass. The system then seamlessly integrates the extracted defects with a flawless background using Poisson editing and outputs the location information of the defects and the label output to automatically generate the dataset. The deep learning network YOLOv5 works as the core algorithm framework, into which the Constructive Block Attention Module and the small target detection layer are specifically added to enhance the capability of the model to detect small defects. According to the experimental results, the combined lighting effectively improves the precision of detecting dust and bright spots. Additionally, with the adoption of novel data augmentation techniques, the enhanced YOLOv5 model is capable of effectively addressing the challenges posed by inefficient sample data and non-uniform distribution, thus mitigating network generalization issues. Furthermore, this data augmentation approach facilitates the rapid adaptation of the same detection tasks to diverse environmental scenarios, enabling the expedited and efficient deployment of the model across various industrial settings. The mean average precision (MAP) of the optimal model in the validation set reached 98.36%, 2.62% higher than that of the original YOLOv5. In addition, its false acceptance rate (FAR) is 1.27%, its false rejection rate (FRR) was 2.47%, its detection speed was 64 fps, and its correct detection rate in the validation set was 98.75%, which meets the current industrial detection requirements by and large. In this way, this paper achieved the automated inspection of mobile phone flat glass with high robustness, high precision, and a low false acceptance rate and false rejection rate, significantly reducing material losses in the factories and the likelihood of error occurrence in follow-on products. This method can be applied to the multi-scale and multi-type detection of glass defects.

Dual-Structure Elements Morphological Filtering and Local Z-Score Normalization for Infrared Small Target Detection against Heavy Clouds

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems.

Multi-scale Small Target Detection for Indoor Mobile Rescue Vehicles Based on Improved YOLOv5

Multi-scale Small Target Detection for Indoor Mobile Rescue Vehicles Based on Improved YOLOv5

Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network

Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network

Multi-Branch Attention Fusion Network for Cloud and Cloud Shadow Segmentation

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network’s focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques.

A defect detection method for industrial aluminum sheet surface based on improved YOLOv8 algorithm

In industrial aluminum sheet surface defect detection, false detection, missed detection, and low efficiency are prevalent challenges. Therefore, this paper introduces an improved YOLOv8 algorithm to address these issues. Specifically, the C2f-DSConv module incorporated enhances the network’s feature extraction capabilities, and a small target detection layer (160 × 160) improves the recognition of small targets. Besides, the DyHead dynamic detection head augments target representation, and MPDIoU replaces the regression loss function to refine detection accuracy. The improved algorithm is named YOLOv8n-DSDM, with experimental evaluations on an industrial aluminum sheet surface defect dataset demonstrating its effectiveness. YOLOv8n-DSDM achieves an average mean average precision (mAP50%) of 94.7%, demonstrating a 3.5% improvement over the original YOLOv8n. With a single-frame detection time of 2.5 ms and a parameter count of 3.77 M, YOLOv8n-DSDM meets the real-time detection requirements for industrial applications.

AFE-YOLOv8: A Novel Object Detection Model for Unmanned Aerial Vehicle Scenes with Adaptive Feature Enhancement

Object detection in unmanned aerial vehicle (UAV) scenes is a challenging task due to the varying scales and complexities of targets. To address this, we propose a novel object detection model, AFE-YOLOv8, which integrates three innovative modules: the Multi-scale Nonlinear Fusion Module (MNFM), the Adaptive Feature Enhancement Module (AFEM), and the Receptive Field Expansion Module (RFEM). The MNFM introduces nonlinear mapping by exploiting the property that deformable convolution can dynamically adjust the shape of the convolution kernel according to the shape of the target, and it effectively enhances the feature extraction capability of the backbone network by integrating multi-scale feature maps from different mapping branches. Meanwhile, the AFEM introduces an adaptive fusion factor, and through the fusion factor, it adaptively integrates the small-target features contained in the feature maps of different detection branches into the small-target detection branch, thus enhancing the expression of the small-target features contained in the feature maps of the small-target detection branch. Furthermore, the RFEM expands the receptive field of the feature maps of the large- and medium-scale target detection branches through stacked convolution, so as to make the model’s receptive field cover the whole target, and thereby learn more rich and comprehensive features of the target. The experimental results demonstrate the superior performance of the proposed model compared to the baseline in detecting objects of various scales. On the VisDrone dataset, the proposed model achieves a 4.5% enhancement in mean average precision (mAP) and a 5.45% improvement in average precision at an IOU threshold of 0.5 (AP50). Additionally, ablation experiments conducted on the challenging DOTA dataset showcase the model’s robustness and generalization capabilities.

Application of pseudo-3D sub-bottom profile imaging technology in small submarine target detection

Application of pseudo-3D sub-bottom profile imaging technology in small submarine target detection

A lightweight fire detection algorithm for small targets based on YOLOv5s

In response to the current challenges fire detection algorithms encounter, including low detection accuracy and limited recognition rates for small fire targets in complex environments, we present a lightweight fire detection algorithm based on an improved YOLOv5s. The introduction of the CoT (Contextual Transformer) structure into the backbone neural network, along with the creation of the novel CSP1_CoT (Cross stage partial 1_contextual transformer) module, has effectively reduced the model’s parameter count while simultaneously enhancing the feature extraction and fusion capabilities of the backbone network; The network’s Neck architecture has been extended by introducing a dedicated detection layer tailored for small targets and incorporating the SE (Squeeze-and-Excitation) attention mechanism. This augmentation, while minimizing parameter proliferation, has significantly bolstered the interaction of multi-feature information, resulting in an enhanced small target detection capability; The substitution of the original loss function with the Focal-EIoU (Focal-Efficient IoU) loss function has yielded a further improvement in the model’s convergence speed and precision; The experimental results indicate that the modified model achieves an mAP@.5 of 96% and an accuracy of 94.8%, marking improvements of 8.8% and 8.9%, respectively, over the original model. Furthermore, the model’s parameter count has been reduced by 1.1%, resulting in a compact model size of only 14.6MB. Additionally, the detection speed has reached 85 FPS (Frames Per Second), thus satisfying real-time detection requirements. This enhancement in precision and accuracy, while simultaneously meeting real-time and lightweight constraints, effectively caters to the demands of fire detection.