Target Detection Research Articles

A Lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU on YOLOv8

The underwater target detection is the most important part of monitoring for environment, ocean, and other fields. However, the detection accuracy is greatly decreased by the poor image quality resulted from the complex underwater environments. The storage and computing power of underwater equipments are not enough for complex underwater target detection technology. Therefore, many YOLO series algorithms have been applied to underwater target detection. On the basis of YOLOv8, a lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU, AGW-YOLOv8, is proposed in this paper. Firstly, by the combination of limited contrast adaptive histogram equalization and wavelet transform(LCAHE-WT), the fidelity and detail of images are improved; Secondly, by CBAM, the key channel features can be effectively extracted with retaining spatial information to improve the performance of the network when dealing with complex image tasks; Thirdly, by GSConv, composed of depth-wise separable convolution and regular convolution, the model parameters and computational complexity are reduced; Fourth, the SE attention mechanism is integrated into the C2f module of the neck, and the channel dimension is weighted to make the network more focus on important features, and to further enhance the feature extraction capability; Finally, by the dynamic nonmonotonic mechanism of WIoU, the gradient gain can be reasonably distributed, the harmful gradients of extreme samples can be reduced, and the generalization ability and overall performance of the model are improved. By the experiments on the URPC2020 data-set, it can been proved that the mAP of AGW-YOLOv8 can reaches 82.9%, is 2.5% higher than that of YOLOv8; and the parameters is 2.95M, is lower than 3.01M of YOLOv8.

Advancing Ton-Bag Detection in Seaport Logistics with an Enhanced YOLOv8 Algorithm

Intelligent logistics and freight transportation is an important part of realizing the intelligence of port terminals. Due to the problems of inaccurate ton bag identification, high costs, large model sizes, and long computation times in traditional freight transportation—issues that hinder meeting real-time requirements on resource-constrained operational equipment—this paper proposes an improved lightweight ton bag detection algorithm, YOLOv8-TB (YOLOv8-Ton Bag), which is optimized based on YOLOv8. Firstly, the improved LZKAC module is introduced to combine with SPPF to form a new SPPFLKZ module, which improves the feature expression performance. Then, with reference to spatial and channel reconstruction convolution and deformable convolution, the C2f-SCTT block is designed for the backbone network, which reduces the spatial and channel redundancy between features in the network. Finally, the C2f-ORECZ block based on a linear scaling layer is designed for the neck, which reduces the training overhead and strengthens the feature learning of the feature extraction network for the targets in the complex background of the harbor and adds the 160 × 160 scale detection head to strengthen small target detection abilities. On the logistics ton bag operation dataset provided by shipping port enterprises, the improved algorithm improves by 3.7% and 5% compared with the original algorithm in mAP50 and mAP50-95, respectively, the model size is reduced by 4.42 MB and the amount of model computation is only 8 G, which is capable of accurately detecting logistics ton bags in real time. The superiority of the method is verified by comparing it with other classical target detection algorithms.

Advancements in LAMP-Based Diagnostics: Emerging Techniques and Applications in Viral Detection with a Focus on Herpesviruses in Transplant Patient Management.

Loop-mediated isothermal amplification (LAMP) is a highly effective molecular diagnostic technique, particularly advantageous for point-of-care (POC) settings. In recent years, LAMP has expanded to include various adaptations such as DARQ-LAMP, QUASR, FLOS-LAMP, displacement probes and molecular beacons. These methods enable multiplex detection of multiple targets in a single reaction, enhancing cost-effectiveness and diagnostic efficiency. Consequently, LAMP has gained significant traction in diagnosing diverse viruses, notably during the COVID-19 pandemic. However, its application for detecting Herpesviridae remains relatively unexplored. This group of viruses is of particular interest due to their latency and potential reactivation, crucial for immunocompromised patients, including organ and hematopoietic stem cell transplant recipients. This review highlights recent advancements in LAMP for virus diagnosis and explores current research trends and future prospects, emphasizing the detection challenges posed by Herpesviridae.

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.

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.

How Helpful May Be a CRISPR/Cas-Based System for Food Traceability?

Genome editing (GE) technologies have the potential to completely transform breeding and biotechnology applied to crop species, contributing to the advancement of modern agriculture and influencing the market structure. To date, the GE-toolboxes include several distinct platforms able to induce site-specific and predetermined genomic modifications, introducing changes within the existing genetic blueprint of an organism. For these reasons, the GE-derived approaches are considered like new plant breeding methods, known also as New Breeding Techniques (NBTs). Particularly, the GE-based on CRISPR/Cas technology represents a considerable improvement forward biotech-related techniques, being highly sensitive, precise/accurate, and straightforward for targeted gene editing in a reliable and reproducible way, with numerous applications in food-related plants. Furthermore, numerous examples of CRISPR/Cas system exploitation for non-editing purposes, ranging from cell imaging to gene expression regulation and DNA assembly, are also increasing, together with recent engagements in target and multiple chemical detection. This manuscript aims, after providing a general overview, to focus attention on the main advances of CRISPR/Cas-based systems into new frontiers of non-editing, presenting and discussing the associated implications and their relative impacts on molecular traceability, an aspect closely related to food safety, which increasingly arouses general interest within public opinion and the scientific community.

Feature detection of B-ultrasound images of intussusception in children based on improved YOLOv8n

To assist grassroots sonographers in accurately and rapidly detecting intussusception lesions from children's abdominal ultrasound images, this paper proposes an improved YOLOv8n children's intussusception detection algorithm, called EMC-YOLOv8n. Firstly, the EfficientViT network with a cascaded group attention module was used as the backbone network to enhance the speed of target detection. Secondly, the improved C2fMBC module was used to replace the C2f module in the neck network to reduce network complexity, and the coordinate attention (CA) module was introduced after each C2fMBC module to enhance attention to positional information. Finally, experiments were conducted on the self-built dataset of intussusception in children. The results showed that the recall rate, average detection accuracy (mAP@0.5) and precision of the EMC-YOLOv8n algorithm improved by 3.9%, 2.1% and 0.9%, respectively, compared to the baseline algorithm. Despite slightly increased network parameters and computational load, significant improvements in detection accuracy enable efficient completion of detection tasks, demonstrating substantial economic and social value.

Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models

To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications.

Global induced local network for infrared: dim small target detection

Abstract It is challenging to detect infrared dim targets submerged in complicated backgrounds due to their small size and faint intensity. The previous attention-based detection networks frequently require global long-range dependence. Significant calculations are required to determine the target's sparse but meaningful position. To prevent wasting calculations on the background, this paper offers a detection network guided by global context for local feature learning, named GILNet (Global Induced Local Network). It designs a global location module (GLM) and a local feature interaction module (LFIM) to capture the global position and features of targets, respectively. More specifically, using global context interaction, the GLM finds the region that might contain dim small targets, that is, the coarse location. In the coarsely located regions, the LFIM further acquires feature information about targets. Next, we also design an eight-directional attention operation to obtain the contour information of targets in the low feature map. It is fused with the high feature map in the multi-directional feature fusion module (MFFM), which retains more semantic and spatial information about targets. Finally, quantitative and qualitative analysis show that the GILNet performs better than eight comparison methods on two public datasets.

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

Highly sensitive multiplexed colorimetric lateral flow immunoassay by plasmon-controlled metal-silica isoform nanocomposites: PINs.

Lateral flow assay (LFA) systems use metal nanoparticles for rapid and convenient target detection and are extensively studied for the diagnostics of various diseases. Gold nanoparticles (AuNPs) are often used as probes in LFAs, displaying a single red color. However, there is a high demand for colorimetric LFAs to detect multiple biomarkers, requiring the use of multicolored NPs. Here, we present a highly sensitive multiplexed colorimetric lateral flow immunoassay by multicolored Plasmon-controlled metal-silica Isoform Nanocomposites (PINs). We utilized the localized surface plasmon resonance effect to create multi-colored PINs by precisely adjusting the distance between the NPs on the surface of PINs through the controlled addition of reduced gold and silver precursors. Through simulations, we also confirmed that the distance between nanoparticles on the surface of PINs significantly affects the color and colorimetric signal intensity of the PINs. We achieved multicolored PINs that exhibit stronger colorimetric signals, offering a new solution for LFA detection with high sensitivity and a 33 times reduced limit of detection (LOD) while maintaining consistent size deviations within 5%. We expect that our PINs-based colorimetric LFA will facilitate the sensitive and simultaneous detection of multiple biomarkers in point-of-care testing.

Small Target Detection Algorithm Based on Improved YOLOv5

Small targets exist in large numbers in various fields. They are broadly used in aerospace, video monitoring, and industrial detection. However, because of its tiny dimensions and modest resolution, the precision of small-target detection is low, and the erroneous detection rate is high. Therefore, based on YOLOv5, an improved small-target detection model is proposed. First, in order to improve the number of tiny targets detected while enhancing small-target detection performance, an additional detection head is added. Second, involution is used between the backbone and neck to increase the channel information of feature mapping. Third, the model introduces the BiFormer, wherein both the global and local feature information are captured simultaneously by means of its double-layer routing attention mechanism. Finally, a context augmentation module (CAM) is inserted into the neck in order to maximize the structure of feature fusion. In addition, in order to consider among the required real frame as well as the prediction frame simultaneously, YOLOv5’s original loss function is exchanged. The experimental results using the public dataset VisDrone2019 show that the proposed model has P increased by 13.43%, R increased by 11.28%, and mAP@.5 and mAP@[.5:.95] increased by 13.88% and 9.01%, respectively.

Research on X-ray security contraband identification technology based on lightweight YOLOv8

Aiming at the difficulty of identification and localization in the detection of contraband targets in X-ray images, as well as the demand for upgrading and deployment of security equipment during the peak flow stage, a lightweight X-ray security contraband detection algorithm is proposed and optimized for YOLOv8. Firstly, the number of channels in the original network is reduced according to a predetermined ratio, which effectively reduces model parameters and computational complexity and achieves better results than the current mainstream lightweight methods, such as model pruning and detection backbone optimization. Secondly, due to the presence of objects of various scales and sizes in X-ray security inspection images, we introduced the BiFPN feature extraction strategy in the neck. We designed a simplified version of BiFPN based on the YOLOv8 network structure to effectively integrate feature information from different levels and scales. Finally, we incorporated the idea of Focal Loss to address the issue of imbalance between positive and negative samples and proposed a new regression loss function, Focal-GIoU Loss, which combines GIoU Loss. This increases the loss weight for important but difficult-to-detect samples, such as small targets and overlapping targets, making the model more focused on these critical samples. Results show that our proposed YOLO-CBF improves mAP by 1.5%, 0.9%, and 0.5% on three datasets with different levels of occlusion in PIDray, while reducing the parameter count and computational cost from the original 3.0M and 8.1G to 2.1M and 6.3G.Moreover, it performs well in comparative experiments between different models and in generalization experiments across different datasets.

Rational MOF Membrane Design for Gas Detection in Complex Environments.

Metal-organic frameworks (MOFs) hold significant promise in the realm of gas sensing. However, current understanding of their sensing mechanisms remains limited. Furthermore, the large-scale fabrication of MOFs is hampered by their inadequate mechanical properties. These two challenges contribute to the sluggish development of MOF-based gas-sensing materials. In this review, the selection of metal ions and organic ligands for designing MOFs is first presented, deepening the understanding of the interactions between different metal ions/organic ligands and target gases. Subsequently, the typical interfacial synthesis strategies (gas-solid, gas-liquid, solid-liquid interfaces) are provided, highlighting the potential for constructing MOF membranes on superhydrophobic and/or superhydrophilic substrates. Then, a multi-scale structure design strategies is proposed, including multi-dimensional membrane design and heterogeneous membrane design, to improve sensing performance through enhanced interfacial mass transfer and specific gas sieving. This strategy is anticipated to augment the task-specific capabilities of MOF-based materials in complex environments. Finally, several key future research directions are outlined with the aim not only to further investigate the underlying sensing principles of MOF membranes but also to achieve efficient detection of target gases amidst interfering gases and elevated moisture levels.

SDFSD-v1.0: A Sub-Meter SAR Dataset for Fine-Grained Ship Detection

In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. Datasets constructed with commonly used slice-based annotation methods suffer from a lack of scalability and low efficiency in repeated editing and reuse. Existing SAR ship datasets mostly consist of medium to low resolution imagery, leading to coarse ship categories and limited background scenarios. We developed the “annotate entire image, then slice” workflow (AEISW) and constructed a sub-meter SAR fine-grained ship detection dataset (SDFSD) by using 846 sub-meter SAR images that include 96,921 ship instances of 15 ship types across 35,787 slices. The data cover major ports and shipping routes globally, with varied and complex backgrounds, offering diverse annotation information. Several State-of-the-Art rotational detection models were used to evaluate the dataset, providing a baseline for ship detection and fine-grained ship detection. The SDFSD is a high spatial resolution ship detection dataset that could drive advancements in research on ship detection and fine-grained detection in SAR imagery.

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.

TransImg: A Translation Algorithm of Visible-to-Infrared Image Based on Generative Adversarial Network

Infrared images of sensitive targets are difficult to obtain and cannot meet the design and training needs of target detection and tracking algorithms for mobile platforms such as aircraft. This paper proposes an image translation algorithm TransImg, which can achieve visible light image translation to the infrared domain to enrich the dataset. First, the algorithm designed a generator structure consisting of a deep residual connected encoder and a region perception feature fusion module to enhance feature learning, thereby avoiding issues such as generating infrared images with insufficient details in the transfer task. Afterward, a multi-scale discriminator and a composite loss function were designed to further improve the transfer effect. Finally, an automatic mixed-precision training strategy was designed for the overall migration algorithm architecture to accelerate the training and generation of infrared images. Experiments have shown that the image translation algorithm TransImg has good algorithm accuracy, and the infrared image generated by visible light image translation has richer texture details, faster generation speed, and lower video memory consumption, and the performance exceeds the mainstream traditional algorithm, and the generated images can meet the requirements of target detection and tracking algorithms design and training for mobile platforms such as aircraft.

Object Detection Algorithms for Video Surveillance Applications

Abstract—Object Detection algorithms find applications in various fields, such as defense, security, and healthcare. In this paper, various Object Detection Algorithms, such as face detection, skin detection, color detection, shape detection, and target detection, are simulated and implemented using MATLAB 2017b to detect various types of objects with improved accuracy for video surveillance applications. Further, various challenges and applications of object detection methods are elaborated.

GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection.

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model's parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model's adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset's Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model's parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos.

Distributed Multi-Sensor Fusion for Multi-Group/Extended Target Tracking with Different Limited Fields of View

The identification of sensing group targets and extended targets is of paramount importance in the context of vehicle tracking and early warning detection. As the scope of target monitoring and tracking extends, conventional single-sensor-based tracking techniques are proving to be inadequate in meeting the practical demands of the field. Consequently, multi-sensor fusion tracking technology has emerged as a viable alternative. However, the use of multiple sensors is constrained by their limited fields of view (FOVs), which leads to issues such as the loss of target detection and the introduction of false targets after fusion. Hence, this study proposes a combination of weighted geometric averaging (WGA) and weighted arithmetic averaging (WAA) methods to solve distributed multi-group/extended target tracking with different fields of view. Specifically, a local Poisson multi-Bernoulli mixture (PMBM) filter was first used on individual sensors. Subsequently, we combined a sequential fusion technique and the proposed fusion approach to fuse the PMBM filter densities. The efficacy and superiority of this approach were demonstrated through simulations.