High-precision Detection Research Articles

High-precision detection of laser focal position based on image processing forming by microlens array

High-precision detection of laser focal position based on image processing forming by microlens array

Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model.

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly in the analysis of high-precision remote sensing images. This challenge is particularly pronounced in scenarios involving numerous small objects, intricate backgrounds, and occluded overlaps. To address these issues, we introduce the Drone-DETR model, which is based on RT-DETR. To overcome the difficulties associated with detecting small objects and reducing redundant computations arising from complex backgrounds in ultra-wide-angle images, we propose the Effective Small Object Detection Network (ESDNet). This network preserves detailed information about small objects, reduces redundant computations, and adopts a lightweight architecture. Furthermore, we introduce the Enhanced Dual-Path Feature Fusion Attention Module (EDF-FAM) within the neck network. This module is specifically designed to enhance the network's ability to handle multi-scale objects. We employ a dynamic competitive learning strategy to enhance the model's capability to efficiently fuse multi-scale features. Additionally, we incorporate the P2 shallow feature layer from the ESDNet into the neck network to enhance the model's ability to fuse small-object features, thereby enhancing the accuracy of small object detection. Experimental results indicate that the Drone-DETR model achieves an mAP50 of 53.9% with only 28.7 million parameters on the VisDrone2019 dataset, representing an 8.1% enhancement over RT-DETR-R18.

YOLO-SAG: An improved wildlife object detection algorithm based on YOLOv8n

Wildlife conservation is crucial for maintaining biodiversity, ensuring ecosystem balance and stability, and fostering sustainable development. Currently, the use of infrared camera traps to monitor and capture photos of wildlife is a vital methodology in protecting and researching wildlife, and automatic detection and identification of animals within captured photographs are paramount. However, factors such as the complexity of the field environment and the varying sizes of animal targets lead to low detection accuracy, while high-precision detection models are hindered by high computational complexity and sluggish training speeds. This paper proposes a wildlife target detection algorithm based on improved YOLOv8n - YOLO-SAG, which aims to balance accuracy and speed. Training stability is enhanced by introducing the Softplus activation function, which increases detection accuracy; incorporating the AIFI enhances intra-scale feature interaction, reducing missed and false detections. Integrating the GSConv and VoV-GSCSP module lightens neck convolutions, reducing computational redundancy and balancing the computational and parametric quantities brought by the AIFI. Experimental results on a self-made wildlife dataset indicate that the YOLO-SAG achieves 94.9%, 90.9%, 96.8%, and 79.9% in Precision, Recall, mAP@0.5, and mAP@0.5–0.95, respectively, which are 3.4%, 3.3%, 3.2%, and 4.9% higher than the original YOLOv8n. Inference and post-processing times reach 1.2 ms and 0.5 ms, a speedup of 25% and 54.5%, respectively, and the computation volume is only 7.2 GFLOPs, an 11.1% decrease.

Calibration and validation of a multi-beam LiDAR onboard China Chang’e lunar probe for landing obstacle avoidance

For automatic exploration of specific areas with high scientific value on the moon and other planets, high-precision obstacle detection and hazard avoidance capabilities are required. Three-dimensional (3D) LiDAR can obtain a high-resolution 3D point cloud, offering significant advantages in landing obstacle avoidance. During the landing of the Chang’e lander, a laser 3D imaging sensor (L3DIS) is used to measure and generate accurate topographic maps of the candidate landing area in real-time to help find a safe landing zone. The L3DIS employs 16 beamlets split from a Gaussian laser beam and 16 channels of linear-array avalanche photodiodes within the same optical path and scans the object with a two-axis galvanometer in a field of view of 29°×33°. The calibration of the systematic errors and the performance of obstacle detection on this sensor are conducted in this paper. First, the instrument components and main error sources of the sensor are introduced, and the systematic errors are calibrated based on planar targets, indicating that the sensor’s accuracy is greater than 4 cm, and the corresponding obstacle detection accuracy is better than 12 cm. Second, the ground validation of obstacle detection is demonstrated, and the obtained point cloud is compared with that obtained by a terrestrial laser scanner, indicating that the sensor has good performance. Finally, the performance of onboard measurement and obstacle detection is analyzed, showing that the sensor can identify obvious craters and rocks, i.e., nine major craters with a maximum and minimum depth of 1.36 m and 0.16 m, respectively, and major rocks with a maximum and minimum height of 0.1 m and 0.05 m, respectively, and main landing area with slop less than 10° except for the edges of craters and rocks.

Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing

Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results.

FC-YOLO: an aircraft skin defect detection algorithm based on multi-scale collaborative feature fusion

Abstract Aircraft skin defects pose a threat to the safety and airworthiness of the aircraft. The front line of engineering has requirements of high precision and stable defect detection, which cannot be met by existing deep learning methods, due to conflicting information between multi-scale features. Herein, a Fine-Coordinated YOLO (FC-YOLO) algorithm is proposed to detect aircraft skin defects. Firstly, the ELAN-C module with Coordinate & Channel Attention mechanism is applied to the backbone network to enhance multi-scale detection precision. Secondly, the Adaptive-Path Aggregation Network structure is proposed to make features containing more information by adding a shortcut weighted by the Adaptively Spatial Feature Fusion (ASFF) module. The ASFF adaptively allocates the weights of features with different sizes to reduce the inconsistency of features between different levels during feature fusion to improve detection precision. Finally, the SCYLLA-IoU loss function is introduced to calculate the directional loss between the bounding box and the ground truth box to elevate the stability of the training. Experiments are executed with a self-constructed ASD-DET dataset and the public NEU-DET dataset. Results show that the mAP of FC-YOLO is improved by 3.1% and 2.7% compared to that of the original YOLOv7 on the ASD-DET dataset and the NEU-DET dataset. In addition, on the ASD-DET dataset and NEU-DET dataset, the mAP of FC-YOLO was higher than that of YOLOv8, RT-DETR by 1.4%, 1.6% and 2.2%, 3.8%, respectively. By which, it is shown that the proposed FC-YOLO algorithm is promising for the future automatic visual inspection of aircraft skin.

MFNet: Multi-scale feature enhancement networks for wheat head detection and counting in complex scene

Developing a high-precision wheat head detection algorithm is challenging due to the dense distribution and diverse sizes of wheat head in the field, as well as serious coverage from weeds. In this work, we propose multi-scale feature enhancement networks for wheat head detection and counting in complex scene (MFNet). Firstly, we introduce a deformable spatial attention mechanism (DSAM) and embed it in the backbone network to enhance the extraction of wheat head features while suppressing irrelevant features, effectively improving detection of wheat head in occluded environments. Secondly, we design a multi-scale receptive field feature fusion (MRFF) module in combination with an improved light-weight feature pyramid module to achieve more accurate detection of wheat head of different sizes and improve localization accuracy simultaneously. Additionally, the modified detection head with deformable convolution is able to adapt to different shapes of wheat head features and accurately predict the bounding boxes. Our method achieves 94.2% AP@50 at a speed of 30 FPS on the GWHD dataset, and to verify the generalization of the proposed method, we constructed a dense wheat head detection (DWHD) dataset with annotations, conducted experiments on the DWHD and SPIKE datasets and compared them with state-of-the-art algorithms. The experimental results show that the proposed method outperformed most of the existing methods, which further proved the superiority and robustness of the proposed method, and demonstrated that our model possesses excellent adaptability, enabling it to flexibly cope with scenarios of significant scale differences and severe occlusion in complex field environments for wheat head, and provide a technological reference for monitoring the wheat phenotype.

Yolov7-Tinier: Towards High-Precision and Lightweight Detection of Fabric Defects in Textile Plant

Yolov7-Tinier: Towards High-Precision and Lightweight Detection of Fabric Defects in Textile Plant

A Historical Handwritten French Manuscripts Text Detection Method in Full Pages

Historical handwritten manuscripts pose challenges to automated recognition techniques due to their unique handwriting styles and cultural backgrounds. In order to solve the problems of complex text word misdetection, omission, and insufficient detection of wide-pitch curved text, this study proposes a high-precision text detection method based on improved YOLOv8s. Firstly, the Swin Transformer is used to replace C2f at the end of the backbone network to solve the shortcomings of fine-grained information loss and insufficient learning features in text word detection. Secondly, the Dysample (Dynamic Upsampling Operator) method is used to retain more detailed features of the target and overcome the shortcomings of information loss in traditional upsampling to realize the text detection task for dense targets. Then, the LSK (Large Selective Kernel) module is added to the detection head to dynamically adjust the feature extraction receptive field, which solves the cases of extreme aspect ratio words, unfocused small text, and complex shape text in text detection. Finally, in order to overcome the CIOU (Complete Intersection Over Union) loss in target box regression with unclear aspect ratio, insensitive to size change, and insufficient correlation between target coordinates, Gaussian Wasserstein Distance (GWD) is introduced to modify the regression loss to measure the similarity between the two bounding boxes in order to obtain high-quality bounding boxes. Compared with the State-of-the-Art methods, the proposed method achieves optimal performance in text detection, with the precision and mAP@0.5 reaching 86.3% and 82.4%, which are 8.1% and 6.7% higher than the original method, respectively. The advancement of each module is verified by ablation experiments. The experimental results show that the method proposed in this study can effectively realize complex text detection and provide a powerful technical means for historical manuscript reproduction.

High-precision detection of dibutyl hydroxytoluene in edible oil via convolutional autoencoder compressed Fourier-transform near-infrared spectroscopy

The quality of edible oils is closely related to their chemical compositions. Antioxidants have widespread application in edible oil production. In this study, a pioneering detection approach involving the use of a one-dimensional convolutional autoencoder (1D-CAE) was introduced to compress spectral data for assessing antioxidant levels in edible oils. Fourier-transform near-infrared (FT-NIR) characterisation of edible oil samples with varying antioxidant concentrations was also conducted. An 1D-CAE model was developed to compress different pre-processed spectra into a condensed representation. These compressed features were then integrated with a support vector machine and partial least squares regression models to establish correlations for each target. The study examined the influence of pre-processing steps and feature engineering methods on near-infrared spectral analysis through independent or combined model analysis. The findings revealed that features derived from the 1D-CAE model demonstrated remarkable repeatability and can be utilised to construct robust detection models. The experimental results showed that the optimal detection model derived based on the 1D-CAE compression features has an average R2, RPD and RMSE of 0.9953, 15.1664 and 1.2035, respectively, on the prediction set. FT-NIR spectroscopy can be used to accurately detect butylated hydroxytoluene in edible oils. Therefore, autoencoders are an effective tool in spectroscopic analysis, offering promising avenues for future research and application.

Efficient magnetic forward modeling for strongly magnetized bodies: An iterative integral equation approach

Magnetic surveying encounters challenges in high-precision detection and inversion on strongly magnetized bodies. Nonuniform magnetization caused by self-demagnetization and mutual magnetization makes magnetic field calculation using analytical formulas impractical. The previous discrete numerical methods have suffered from computational inefficiency or low accuracy. To resolve this issue, we develop an algorithm to solve the magnetic field integral equation for calculating the magnetization state of high-susceptibility bodies, combining the improved spatial convolution forward approach with the adaptive relaxation iteration method. To address the excessive computational burden in the magnetization field between 3D voxel arrays of the discrete model, we construct the circular convolution kernel matrix directly when the subsurface space is divided with an equidistant grid, significantly reducing redundant computations. Then, the fast Fourier transform is used to accelerate the forward discrete circular convolution operation. In addition, we derive an iterative computation scheme that adaptively adjusts the relaxation factor based on magnetic field changes to correct the effective magnetization for algorithm convergence. From a pragmatic perspective, equating the cuboid to equal-volume sphere subdivisions is developed to enhance computational efficiency further by approximately 50% despite sacrificing slight accuracy. The sphere and spherical shell models validate the algorithm accuracy, efficiency, and convergence. Furthermore, we analyze the characteristics of the mutual magnetization effect among magnetic bodies under different magnetization conditions. Finally, the applicability of the algorithm is demonstrated with a realistic example. Our algorithm shows promise for precisely exploring highly magnetized minerals, underground pipelines, and unexploded ordnance.

Pavement Crack Detection Using Fractal Dimension and Semi-Supervised Learning

Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method’s effectiveness in identifying cracks in complex environments.

Ship Detection in Synthetic Aperture Radar Images Based on BiLevel Spatial Attention and Deep Poly Kernel Network

Synthetic aperture radar (SAR) is a technique widely used in the field of ship detection. However, due to the high ship density, fore-ground-background imbalance, and varying target sizes, achieving lightweight and high-precision multiscale ship object detection remains a significant challenge. In response to these challenges, this research presents YOLO-MSD, a multiscale SAR ship detection method. Firstly, we propose a Deep Poly Kernel Backbone Network (DPK-Net) that utilizes the Optimized Convolution (OC) Module to reduce data redundancy and the Poly Kernel (PK) Module to improve the feature extraction capability and scale adaptability. Secondly, we design a BiLevel Spatial Attention Module (BSAM), which consists of the BiLevel Routing Attention (BRA) and the Spatial Attention Module. The BRA is first utilized to capture global information. Then, the Spatial Attention Module is used to improve the network’s ability to localize the target and capture high-quality detailed information. Finally, we adopt a Powerful-IoU (P-IoU) loss function, which can adjust to the ship size adaptively, effectively guiding the anchor box to achieve faster and more accurate detection. Using HRSID and SSDD as experimental datasets, mAP of 90.2% and 98.8% are achieved, respectively, outperforming the baseline by 5.9% and 6.2% with a model size of 12.3 M. Furthermore, the network exhibits excellent performance across various ship scales.

YOLO-PEM: A Lightweight Detection Method for Young “Okubo” Peaches in Complex Orchard Environments

The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve high-precision and automatic detection of young “Okubo” peaches. Firstly, the C2f_P module was devised by partial convolution (PConv), replacing all C2f modules in YOLOv8s to achieve the model’s lightweight. Secondly, embedding the efficient multi-scale attention (EMA) module in the lightweight C2f_P_1 module of the backbone network enhanced the feature extraction capability and accuracy for young peaches. Finally, the MPDIoU loss function was utilized to replace the original CIoU loss function, which improved the detection accuracy of the bounding box while speeding up the convergence of the model. The experimental results demonstrate that the YOLO-PEM model achieved an average precision (AP) of 90.86%, F1 score of 86.70%, and model size of 16.1 MB, which was a 1.85% improvement in the AP, 0.85% improvement in the F1 score, and 5.3 MB reduction in the model size compared with YOLOv8s. The AP was 6.26%, 6.01%, 2.05%, 2.12%, and 1.87% higher compared with the other lightweight detection models YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, respectively. Furthermore, the FPS of YOLO-PEM was 196.2 f·s-1, which can fulfill the demand for the real-time detection of young peaches. YOLO-PEM effectively detects young peaches in complex orchard environments and can offer a basis for the theoretical design of the vision system of the “Okubo” peach fruit-thinning robot and scientific management of orchards.

Real-time detection system for polishing metal surface defects based on convolutional feature concentration and activation network

This study aims to improve the accuracy and efficiency of detecting defects on polished metal surfaces by developing a new detection system. A network model explicitly designed for detecting defects on polished metal surfaces and a convolutional feature concentration and activation network (CFCANet) are proposed in this paper. This model significantly enhances the recognition of tiny defects by introducing a small-target detection head, ensuring high-precision detection results. In addition, the feature concentration and activation (FCA-C2f) module proposed in this study enhances the model’s sensitivity to anisotropic features, thereby improving defect detection accuracy. The content-aware reassembly of features (CARAFE) upsampling algorithm is used instead of traditional nearest-neighbour interpolation methods to effectively preserve detailed information and improve the quality and efficiency of upsampling. By optimising the lighting conditions and using composite light source illumination technology, the probabilities of missed detections and false alarms can be reduced. Combined with the CFCANet detection network, the defect detection performance of the proposed method on polished metal surfaces is effectively enhanced. To validate the effectiveness of the proposed method, a new dataset for detecting defects on polished metal surfaces, PMS-DET, was constructed in this study and validated on the NEU-DET dataset. Experimental results show that CFCANet effectively improves the defect detection accuracy on polished metal surfaces, achieving a mAP0.5:0.95 value of 42.4 % on the PMS-DET dataset, an increase of 11.9 %. The model parameters are reduced by 6.7 %, and the detection speed is improved by 28.1 %. Compared with existing detection models, this research method demonstrates significant improvements in detection accuracy, model size and computational efficiency, especially regarding GFLOPs and detection speed, proving its potential application value in practical industrial scenarios.

IMC-ResNet: Atrial fibrillation detection method based on interlayer multiscale coupling

Atrial fibrillation(AF) is one of the most common cardiac arrhythmias, and reliable detection of it is of significant importance. This study proposes a multilayer convolutional neural network for AF detection, which introduces the Multiscale Information Interaction module composed of a Detail Enhancer and a Context Information Extractor to achieve the extraction and coupling of multiscale information between layers. The Detail Enhancer is composed of multiscale transposed convolutions to encode waveform details in the electrocardiogram (ECG). The Context Information Extractor is composed of multiscale dilated convolutions to encode the morphological information of the ECG. Furthermore, the Multiscale Information Interaction module is used in conjunction with residual blocks to model the surface feature correlation and intrinsic abstract structure of AF signals. This approach extracts implicit information of AF from ECG signals, achieving high-precision and robust detection of AF. Five-fold cross-validation on the AFDB dataset resulted in an accuracy of 99.56% and an F1-score of 99.65%. Inter-patient testing on the clinical SPHDB database yielded an accuracy of 96.17% and an F1-score of 96.14%. In addition, this study conducted experiments to evaluate the model’s robustness against noise, yielding favorable results across four types of noise tests. The results indicate that the proposed method has great potential for application in clinical auxiliary diagnosis based on wearable devices.

A lightweight and efficient model for grape bunch detection and biophysical anomaly assessment in complex environments based on YOLOv8s.

As one of the most important economic crops, grapes have attracted considerable attention due to their high yield, rich nutritional value, and various health benefits. Identifying grape bunches is crucial for maintaining the quality and quantity of grapes, as well as managing pests and diseases. In recent years, the combination of automated equipment with object detection technology has been instrumental in achieving this. However, existing lightweight object detection algorithms often sacrifice detection precision for processing speed, which may pose obstacles in practical applications. Therefore, this thesis proposes a lightweight detection method named YOLOv8s-grape, which incorporates several effective improvement points, including modified efficient channel attention (MECA), slim-neck, new spatial pyramid pooling fast (NSPPF), dynamic upsampler (DySample), and intersection over union with minimum point distance (MPDIoU). In the proposed method, MECA and NSPPF enhance the feature extraction capability of the backbone, enabling it to better capture crucial information. Slim-neck reduces redundant features, lowers computational complexity, and effectively reuses shallow features to obtain more detailed information, further improving detection precision. DySample achieves excellent performance while maintaining lower computational costs, thus demonstrating high practicality and rapid detection capability. MPDIoU enhances detection precision through faster convergence and more precise regression results. Experimental results show that compared to other methods, this approach performs better in the grapevine bunch detection dataset and grapevine bunch condition detection dataset, with mean average precision (mAP50-95) increasing by 2.4% and 2.6% compared to YOLOv8s, respectively. Meanwhile, the computational complexity and parameters of the method are also reduced, with a decrease of 2.3 Giga floating-point operations per second and 1.5 million parameters. Therefore, it can be concluded that the proposed method, which integrates these improvements, achieves lightweight and high-precision detection, demonstrating its effectiveness in identifying grape bunches and assessing biophysical anomalies.

Detailed height mapping of trees and buildings (HiTAB) in Chicago and its implications to urban climate studies

Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped the height of trees and buildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments.

High-Sensitivity Refractive Index Sensor with Dual-Channel Based on Surface Plasmon Resonance Photonic Crystal Fiber.

In order to achieve a high-precision synchronous detection of two different refractive index (RI) analytes, a D-type surface plasmon resonance (SPR) photonic crystal fiber (PCF) RI sensor based on two channels is designed in this paper. The sensor uses a D-shaped planar region of the PCF and a large circular air hole below the core as the sensing channels. Surface plasmon resonance is induced by applying a coating of gold film on the surface. The full-vector finite-element method (FEM) is used to optimize the structural parameters of the optical fiber, and the sensing characteristics are studied, including wavelength sensitivity, RI resolution, full width at half maximum (FWHM), figure of merit (FOM), and signal-to-noise ratio (SNR). The results show that the channel 1 (Ch 1) can achieve RI detection of 1.36-1.39 in the wavelength range of 1500-2600 nm, and the channel 2 (Ch 2) can achieve RI detection of 1.46-1.57 in the wavelength range of 2100-3000 nm. The two sensing channels can detect independently or simultaneously measure two analytes with different RIs. The maximum wavelength sensitivity of the sensor can reach 30,000 nm/RIU in Channel 1 and 9900 nm/RIU in Channel 2. The RI resolutions of the two channels are 3.54 × 10-6 RIU and 10.88 × 10-6 RIU, respectively. Therefore, the sensor realizes dual-channel high- and low-RI synchronous detection in the ultra-long wavelength band from near-infrared to mid-infrared and achieves an ultra-wide RI detection range and ultra-high wavelength sensitivity. The sensor has a wide application prospect in the fields of chemical detection, biomedical sensing, and water environment monitoring.

Lightweight Water Surface Object Detection Network for Unmanned Surface Vehicles

The detection algorithms for water surface objects considerably assist unmanned surface vehicles in rapidly perceiving their surrounding environment, providing essential environmental information and evaluating object attributes. This study proposes a lightweight water surface target detection algorithm called YOLO-WSD (water surface detection), based on YOLOv8n, to address the need for real-time, high-precision, and lightweight target detection algorithms that can adapt to the rapid changes in the surrounding environment during specific tasks. Initially, we designed the C2F-E module, enriched in gradient flow compared to the conventional C2F module, enabling the backbone network to extract richer multi-level features while maintaining lightness. Additionally, this study redesigns the feature fusion network structure by introducing low-level features and achieving multi-level fusion to enhance the network’s capability of integrating multiple levels. Meanwhile, it investigates the impact of channel number differences in the Concat module fusion on model performance, thereby optimizing the neural network structure. Lastly, it introduces the WIOU localization loss function to bolster model robustness. Experiments demonstrated that YOLO-WSD achieves a 4.6% and 3.4% improvement in mAP0.5 on the water surface object detection dataset and Seaship public dataset, respectively, with recall rates improving by 5.4% and 8.5% relative to the baseline YOLOv8n model. The model’s parameter size is 3.3 M. YOLO-WSD exhibits superior performance compared to other mainstream lightweight algorithms.