Target Detection Method Research Articles

Breaking dimensional barriers in hyperspectral target detection: Atrous convolution with Gramian Angular field representations

Hyperspectral images contain extensive spectral bands with rich spectral information that reflects object properties. Leveraging state-of-the-art deep learning techniques has proven to be effective in hyperspectral target detection. However, compared to two-dimensional matrix data, the one-dimensional nature of spectral sequence limits the information that can be extracted, posing a challenge for deep learning-based hyperspectral target detection methodologies. To address this issue, a novel hyperspectral target detection method employing atrous convolution with gramian angular field representations is proposed in this paper. This approach breaks the barrier between one-dimensional vector and two-dimensional matrix by gramian angular field, transforming the spectral sequences from one-dimensional vectors into two-dimensional matrices, enabling the exploration of multidimensional relationships within spectral band relations through an atrous convolution-based spectral feature extraction network. The proposed model transcends the traditional one-dimensional spectral target detection limitations, offering a new perspective for spectral-based hyperspectral target detection. Experimental results on four real-world hyperspectral datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in detection performance, showcasing its potential for advancing hyperspectral target detection.

A Review of YOLO-Based Target Detection Methods

Target detection is now a popular topic in computer vision. With the development and iteration of technology, deep learning is constantly emerging. The integration of deep learning in the target detection task has led to rapid improvement in accuracy and speed, among which the You Only Look Once (YOLO) series of methods have the most rapid and varied improvements and upgrades, which have been widely used in the fields of navigation, video surveillance, face detection, text detection, and aerospace, etc. This paper initially provides an overview of the research context, importance, and challenges associated with this domain, compares and analyzes the network structure and implementation of the single-phase target detection method represented by the YOLO series with the two-phase and other improved algorithms, and then introduces the research progress of the target detection algorithms of deep learning, the characteristics of the commonly used datasets, and the key parameters of the evaluation of performance indicators, and then presents a compilation of experimental outcomes associated with several widely recognized algorithms applied to prominent datasets. Subsequently, it enumerates the experimental findings of diverse algorithms on these established datasets. Ultimately, this paper anticipates future research trajectories and developmental trends pertaining to target detection algorithms.

Fish-Finder: A robust small target detection method for aquaculture fish in low-quality underwater images.

Underwater fish object detection serves as a pivotal research direction in marine biology, aquaculture management, and computer vision, yet it poses substantial challenges due to the complexity of underwater environments, occultations, and the small-sized and frequently moving fish in aquaculture. Addressing these challenges, we propose a novel underwater fish object detection algorithm named Fish-Finder. First, we engendered a structure titled "C2fBF," utilizing the dual-path routing attention protocol of BiFormer. The primary objective of this structure is to alleviate the perturbations induced by underwater intricacies during the phase of downsampling in the backbone network, thereby discerning and conserving finer contextual features. Subsequently, we co-opted the RepGFPN method within our neck network-a distinctive approach that adeptly merges high-level semantic constructs with low-level spatial specifics, thus fortifying its multi-scale detection prowess. Then, in an endeavor to diminish the sensitivity toward positional aberrations during the detection of diminutive aquatic creatures, we incorporated a novel bounding box regression loss function, the Wasserstein loss, to the existing CIoU. This innovative function gauges the congruity between the predicted bounding box Gaussian distribution and the reference bounding box Gaussian distribution. Finally, in regard to the dataset, we independently assembled a specific dataset termed "SmallFish." This unique dataset, meticulously designed for the detection of small-scale fish within intricate underwater settings, includes 5000 annotated images of small fish. Experimental results demonstrate that, compared to the state-of-the-art detection methods, our proposed method improves the accuracy by and , and mean average precision (mAP) increases and in public dataset Kaggle-Fish and our SmallFish dataset, respectively.

A low illumination target detection method based on a dynamic gradient gain allocation strategy

Current target detection methods perform well under normal lighting conditions; however, they encounter challenges in effectively extracting features, leading to false detections and missed detections in low illumination environments. To address these issues, this study introduces an efficient target detection method for low illumination, named DimNet. This method optimizes the model through enhancements in multi-scale feature fusion, feature extraction, detection head, and loss function. Firstly, efficient multi-scale feature fusion is performed by using a new neck structure in the original model so that it can fully exchange high-level semantic information and low-level spatial information. Secondly, by designing a new feature aggregation module, it can simultaneously fuse channel and spatial information as well as local and global information to improve the representation of the network. Subsequently, to achieve more accurate target recognition, a new detection head is designed by replacing the original convolutional layer and utilizing the reparameterization technique, which enhances recognition performance in complex scenes. Additionally, the size of the improved detection head is reduced by adopting a parameter-sharing approach, thereby balancing detection accuracy with computational efficiency. Finally, to solve the fuzzy boundary problem caused by the target boundary being similar to the surrounding background due to insufficient illumination under low illumination conditions, a new loss function is designed in this paper, which pays more attention to the center of the target and weakly considers the aspect ratio of the target prediction frame, and at the same time, the new loss function employs a dynamic gradient gain assignment strategy to reduce the effect of the low-quality anchor frames and to improve the target localization Accuracy. The experimental results show that DimNet achieves a mAP50 of 75.60% on the ExDark dataset, which is an improvement of 3.77% over the baseline model and 2.25% over the state-of-the-art (SOTA) model. DimNet outperforms the previous and current SOTA methods in terms of detection accuracy and other aspects of performance, which is a clear advantage.

Enhancing Alignment Accuracy in Laser Wireless Power Transmission Systems Using Integrated Target Detection and Perturbation-Observation Method

Enhancing Alignment Accuracy in Laser Wireless Power Transmission Systems Using Integrated Target Detection and Perturbation-Observation Method

Precision and Efficiency in Dam Crack Inspection: A Lightweight Object Detection Method Based on Joint Distillation for Unmanned Aerial Vehicles (UAVs)

Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very important to ensure their normal operation. Traditional detection methods rely on manual inspection, which consumes a lot of time and labor, while deep learning methods can greatly alleviate this problem. However, previous studies have often focused on how to better detect crack defects, with the corresponding image resolution not being particularly high. In this study, targeting the scenario of real-time detection by drones, we propose an automatic detection method for dam crack targets directly on high-resolution remote sensing images. First, for high-resolution remote sensing images, we designed a sliding window processing method and proposed corresponding methods to eliminate redundant detection frames. Then, we introduced a Gaussian distribution in the loss function to calculate the similarity of predicted frames and incorporated a self-attention mechanism in the spatial pooling module to further enhance the detection performance of crack targets at various scales. Finally, we proposed a pruning-after-distillation scheme, using the compressed model as the student and the pre-compression model as the teacher and proposed a joint distillation method that allows more efficient distillation under this compression relationship between teacher and student models. Ultimately, a high-performance target detection model can be deployed in a more lightweight form for field operations such as UAV patrols. Experimental results show that our method achieves an mAP of 80.4%, with a parameter count of only 0.725 M, providing strong support for future tasks such as UAV field inspections.

Research on multi-scale fusion image enhancement and improved YOLOv5s lightweight ROV underwater target detection method

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields.

Precision Detection of Infrared Small Target in Ground-to-Air Scene

Reliable infrared small target detection plays an important role in infrared search and track systems. In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. When the size of the target is small or the target is close to the background, the statistical features of the rectangular window would reduce the significance of the target. Moreover, such methods have limited effect on interfering targets, high brightness background, background edges, and clutter suppression in complex backgrounds, and are likely to misdetect the target or even miss it. This paper proposes a non-window, structured algorithm for precision detection of infrared small targets under ground-to-air complex scenes. The non-window, structured local grayscale descent intensity and local gradient watershed (LGDI-LGW) filter can detect a 1 × 1 pixel infrared small target, and effectively suppress interfering targets and background edges. By using the adaptive threshold and centroid algorithm on the target area, the precision of target coordinates reaches sub-pixel accuracy. The results of 9 simulation experiments show that the algorithm has the lowest false alarm rate and the highest detection rate compared with the eight baseline algorithms. It can effectively detect targets with Gaussian distribution of grayscale values and targets with grayscale values approximating tree stump structure. The results of 2 engineering experiments show that under simulated near-sun conditions, a uniform target is precisely detected, and the UAV point target is precisely detected in complex ground-to-air scenes.

Attention Pyramid Networks for Object Detection With Semantic Information Fusion

Pyramid context information fusion has limitations in distinguishing background and important semantic information. The attention mechanism solves the limitations, but the detection effect for small target detection and occluded object detection is not good. In order to solve the above problems, a target detection method based on attention pyramid information fusion (APIFNet) is proposed. It consists of Dynamic Pyramid Attention Fusion Module (DPAF) and Attention Semantic Contextual Information Module (ACM). First, the DPAF module fuses information at different scales and simultaneously guides the learning of low-level features by high-level features to enhance semantic information and spatial details. Then, in order to detect small targets and occluded objects, the ACM module effectively enhances the detection ability of small targets by emphasizing the importance of foreground contextual semantic information and suppressing unimportant semantic information. Ultimately, APIFNet outperforms other methods in terms of evaluation performance on the COCO and PASCAL VOC data sets.

Bionic ankle-assisted rehabilitation training system based on biomechanical evaluation

In modern society, people’s life rhythm is getting faster and faster. Ankle injury would significantly reduce the frequency of people’s activities, which has a great impact on people’s normal work and life. As a new medical method, the bionic ankle rehabilitation training system is used to assist rehabilitation doctors to help patients complete joint flexibility and recovery training. In the research method of ankle biomechanical characteristics, the detection of ankle joint patient’s motion is particularly important. The purpose of this paper is to study how to design a bionic ankle assisted rehabilitation training system based on image processing. Therefore, this paper proposes an image-based moving target detection method, which has the advantages of high reliability and simple operation, and can improve the recognition rate of the ankle joint movement. The experimental results of this paper showed that the system can realize the predetermined trajectory movement and run the system stably. In terms of patient following error, it was kept within 0.03cm. The following error of the ankle joint trajectory was up to 0.03cm and the lowest was 0.01cm, which was almost negligible. In terms of accuracy, the accuracy of the system was also very high, and it can respond and determine the patient’s actions quickly, thereby helping patients to better perform rehabilitation training.

Drainage Pipeline Defect Detection System Based on Semantic Segmentation

Since drainage pipes are widely used in daily life and are necessary for cities to function normally, intelligent fault detection in these pipes has emerged as both an urgent necessity and a major area of development. Compared to traditional target detection methods, the semantic segmentation-based pipeline defect detection method has some special qualities. It can effectively detect a variety of pipeline defect types and segment the defects using precise geometrical attributes to support subsequent defect assessment. This study replaces the backbone network and fuses bar pooling and cascade feature fusion modules in the encoding and decoding phases, respectively, to obtain richer semantic information. This approach strikes a balance between segmentation accuracy and model complexity, aiming to address the problems of the large computational volume of semantic segmentation and the loss of detailed semantic information of pipeline defects. In the pipe defect dataset, the proposed approach is compared with the baseline algorithm and the experimental findings indicate that it has a greater segmentation accuracy than the conventional algorithm. Additionally, the lightweight design of the algorithm lowers the model’s complexity.

Multi-class Object Detection in Urban Scenes Based on Deep Learning

Abstract. The rapid development of urbanization presents challenges and requirements for multi-class object detection in urban scenes. Accurately identifying buildings, vehicles, and trees in urban scenes can optimize urban planning, traffic management, monitoring environmental conditions, and promote the development of smart cities. Traditional target detection methods perform poorly in complex urban environments, while deep learning technology achieves accurate target recognition and positioning by automatically extracting high-level semantic features. In this study, we chose to use the YOLOv5s algorithm for multi-class target detection in urban scenes. YOLOv5s is a lightweight deep learning model with small storage space and efficient detection speed. In this paper, the Potsdam area data published by ISPRS is used to make the label data of buildings, vehicles and trees. The YOLOv5s algorithm is used to iteratively train the model. The results show that the mAP value detected by the YOLOv5s model can reach 82.83%. The experimental results show that the algorithm shows higher accuracy than SSD and Faster R-CNN in tree detection. Although it has a slight decline in building and vehicle detection, considering the factors such as detection accuracy, speed, and model size, the YOLOv5s algorithm has a better recognition and detection effect for the detection of multi-class targets in urban scenes.

Small Object Detection with Small Samples Using High-Resolution Remote Sensing Images

Abstract Interpretation of remote sensing images has become a research hotspot in the field of remote sensing in recent years. It is currently widely applied in areas such as mapping, dynamic monitoring, earth resource surveys and geological disaster investigation. Compared to traditional methods, remote sensing image target detection and recognition methods based on deep learning have achieved significant improvements in accuracy. However, these methods often face challenges such as sample scarcity, interference from complex background, limited feature information, and the dependence on discriminative key feature regions for recognizing fine-grained targets. Addressing these challenges, this paper conducts research on small target detection methods using high-resolution remote sensing images. It explores deep learning theories and methods such as feature enhancement and attention mechanisms within a supervised learning framework. The proposed target detection model consists of four parts: Deep feature extraction module, which extracts features of small targets at multiple scales. Feature enhancement module, which enhances the feature differences between the background and small targets at different scales. Target detection module based on enhanced features. Loss function for optimizing network parameters. Experimental validation shows that this model can effectively extract feature information of small targets under sample-scarce conditions, achieving outstanding results in small target detection in remote sensing images.

Intelligent optimization of horizontal wellbore trajectory based on reinforcement learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool's building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

FF-net: A Target Detection Method Tailored for Mid-to-Late Stages of Forest Fires in Complex Environments

To address the suboptimal detection of forest fires in complex environments during the middle and late stages, we have developed a novel object detection algorithm, FF-net (F_Res, Fire Label Assignment). Initially, a novel fire extraction method, F_Res (Fire ResNet), was designed, incorporating an F_fire activation function to enhance the network's feature extraction and nonlinear fitting capabilities. To reduce the complexity of the network while ensuring detection accuracy, a Fire Label Assignment (FLA) method was devised. Ultimately, the Kullback-Leibler (KL) divergence was integrated with the Focal Loss to create a loss function, KLF (Kullback-Leibler Focal Loss), capable of addressing data imbalance and preventing gradient vanishing and explosion. We reconstructed aerial forest fire and flame datasets and, employing 16 evaluative metrics, compared experiments with classical CNN algorithms, SOTA (State-of-the-Art) algorithms, Transformer algorithms, and advanced fire detection algorithms, discovering that the overall detection efficacy of FF-net surpasses that of other algorithms. In terms of precision on the fire dataset, it exceeds the performance of classical CNN algorithms, SOTA algorithms, and Transformer algorithms by 6.46%, 6.40%, and 21.50% respectively. On the flame dataset, the mean accuracy is 2.47% higher than that of the second-ranking algorithm. Subsequently, visualizing the network's performance on both datasets using Grad-CAM once again confirmed the suitability of FF-net for the task of aerial forest fire detection. The source code of the paper is publicly available at: https://github.com/jyyuan666/FF-net.

Subspace‐based distributed target detection method with small training data samples

AbstractDetecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors.

GLIC: Underwater target detection based on global–local information coupling and multi-scale feature fusion

With the rapid development of object detection technology, underwater object detection has attracted widespread attention. Most of the existing underwater target detection methods are built based on convolutional neural networks (CNNs), which still have some limitations in the utilization of global information and cannot fully capture the key information in the images. To overcome the challenge of insufficient global–local feature extraction, an underwater target detector (namely GLIC) based on global–local information coupling and multi-scale feature fusion is proposed in this paper. Our GLIC consists of three main components: spatial pyramid pooling, global–local information coupling, and multi-scale feature fusion. Firstly, we embed spatial pyramid pooling, which improves the robustness of the model while retaining more spatial information. Secondly, we design the feature pyramid network with global–local information coupling. The global context of the transformer branch and the local features of the CNN branch interact with each other to enhance the feature representation. Finally, we construct a Multi-scale Feature Fusion (MFF) module that utilizes balanced semantic features integrated at the same depth for multi-scale feature fusion. In this way, each resolution in the pyramid receives equal information from others, thus balancing the information flow and making the features more discriminative. As demonstrated in comprehensive experiments, our GLIC, respectively, achieves 88.46%, 87.51%, and 74.94% mAP on the URPC2019, URPC2020, and UDD datasets.

A Multi-Tiered Collaborative Network for Optical Remote Sensing Fine-Grained Ship Detection in Foggy Conditions

Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days.

Transmission Line Defect Target-Detection Method Based on GR-YOLOv8.

In view of the low levels of speed and precision associated with fault detection in transmission lines using traditional algorithms due to resource constraints, a transmission line fault target-detection method for YOLOv8 (You Only Look Once version 8) based on the Rep (Representational Pyramid) Visual Transformer and incorporating an ultra-lightweight module is proposed. First, the YOLOv8 detection network was built. In order to address the needs of feature redundancy and high levels of network computation, the Rep Visual Transformer module was introduced in the Neck part to integrate the pixel information associated with the entire image through its multi-head self-attention and enable the model to learn more global image features, thereby improving the computational speed of the model; then, a lightweight GSConv (Grouped and Separated Convolution, a combination of grouped convolution and separated convolution) convolution module was added to the Backbone and Neck to share computing resources among channels and reduce computing time and memory consumption, by which the computational cost and detection performance of the detection network were balanced, while the model remained lightweight and maintained its high precision. Secondly, the loss function Wise-IoU (Intelligent IOU) was introduced as the Bounding-Box Regression (BBR) loss function to optimize the predicted bounding boxes in these grid cells and shift them closer to the real target location, which reduced the harmful gradients caused by low-quality examples and further improved the detection precision of the algorithm. Finally, the algorithm was verified using a data set of 3500 images compiled by a power-supply inspection department over the past four years. The experimental results show that, compared with the seven classic and improved algorithms, the recall rate and average precision of the proposed algorithm were improved by 0.058 and 0.053, respectively, compared with the original YOLOv8 detection network; the floating-point operations per second decreased by 2.3; and the picture detection speed was increased to 114.9 FPS.

MM-IRSTD: Conv Self-Attention-Based Multi-Modal Small and Dim Target Detection in Infrared Dual-Band Images

Infrared multi-band small and dim target detection is an important research direction in the fields of modern remote sensing and military surveillance. However, achieving high-precision detection remains challenging due to the small scale, low contrast of small and dim targets, and their susceptibility to complex background interference. This paper innovatively proposes a dual-band infrared small and dim target detection method (MM-IRSTD). In this framework, we integrate a convolutional self-attention mechanism module and a self-distillation mechanism to achieve end-to-end dual-band infrared small and dim target detection. The Conv-Based Self-Attention module consists of a convolutional self-attention mechanism and a multilayer perceptron, effectively extracting and integrating input features, thereby enhancing the performance and expressive capability of the model. Additionally, this module incorporates a dynamic weight mechanism to achieve adaptive feature fusion, significantly reducing computational complexity and enhancing the model’s global perception capability. During model training, we use a spatial and channel similarity self-distillation mechanism to drive model updates, addressing the similarity discrepancy between long-wave and mid-wave image features extracted through deep learning, thus improving the model’s performance and generalization capability. Furthermore, to better learn and detect edge features in images, this paper designs an edge extraction method based on Sobel. Finally, comparative experiments and ablation studies validate the advancement and effectiveness of our proposed method.