Coordination Modulation Research Articles

Asymmetric coordinative modulation boosting the activity and thermal stability of Pt1/CeO2 for CO oxidation under harsh condition

Supported metal catalysts play a central role in chemical industry, but it remains a formidable challenge to realize high activity while maintaining atomically dispersed status to maximize atom efficiency under harsh conditions. Herein, a novel strategy of constructing asymmetric coordination Pt active sites via coating N-doped carbon onto ceria supported Pt single atoms (Pt1/CeO2@CN) is proposed, through which superior low-temperature CO oxidation activity and satisfactory thermal stability are achieved simultaneously. Experimental and density functional theory results confirm that the unique asymmetric N2-Pt1-O2 coordinative configuration on Pt1/CeO2@CN tailors the electronic properties of Pt 5d states, increasing the turnover frequency by 15 times for CO oxidation at 120 °C compared with that on Pt1/CeO2 and sustaining the atomically dispersed status of Pt at as high as 400 °C under H2-containing atmosphere. This strategy suggests a promising avenue to fabricating highly active and stable supported metal catalysts for practical applications under harsh conditions.

MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism

AbstractAccurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain tumor MRI segmentation network (MRS‐Net) incorporating an attention mechanism to enhance segmentation accuracy. First, the double residual feature fusion module is utilized to enhance the fusion of feature information between different levels. Second, the Atrous Spatial Pyramid Pooling is introduced as a bridging module of the network to capture the features at different scales of the image, so as to enhance the extraction capability of the network for detailed features. Finally, the inverted residual coordinate attention module replaces the direct splicing in Unet to fuse the large feature information at each level and scale, thus enhancing the model's ability to recognize the spatial location information of brain tumors. The Dice coefficients, positive predictive values (PPVs), sensitivities (Sensitivity) and Hausdorff distance (HD), which are the four evaluation indexes, reach 84.54%, 87.43%, 88.37% and 2.248, respectively, which are improved by 1.85%, 2.11%, 2.88% and 6.0%, respectively, compared with Unet. The experimental results show that MRS‐Net achieves better brain tumor image segmentation.

The impact of amorphous-crystal interface on photoresponse in oxide semiconductor InGaZnO4

The photoresponse of an oxide semiconductor is influenced by lattice distortion, which can be adjusted through atomic coordination status. Typically, the modulation of atomic coordination and photoresponse involves introducing oxygen vacancy (VO) density. In addition to VO, the interface exhibits varying degrees of lattice distortion and may offer an efficient means for modulating photoresponse. Therefore, investigating the interface dependence of photoresponse is both warranted and lacking in current research. Herein, we utilize an amorphous-crystal interface in an InGaZnO4 film to modulate its photoresponse. Compared to films without such interfaces, those with interfaces demonstrate a higher illumination-to-dark current ratio and a slower decay rate of photocurrent upon removal of illumination.

Point cloud upsampling network based on pyramid pooling and self-attention mechanism

For the problems of irregular point cloud data and non-unique input size, a point-cloud upsampling network (PPSA-PU) based on Pyramid Pooling and Self-Attention mechanism is proposed. Multi-scale features are first extracted from the input point cloud using dense map convolution with different expansion rates, and the pyramid pooling module and self-attention mechanism are added. By applying the same pooling operation to regions of different sizes and numbers, the network is made to accept inputs of arbitrary size. Self-attention is used to weight the features to establish global correlation and improve the feature extraction capability. The features are then extended by the Self-Attention NodeShuffle (SA-NS) module, which uses a graph convolution layer to fuse neighbour information and learn new point features from the latent space. Finally, the coordinate reconstruction module is used to generate a dense up-sampled point cloud. Using the existing dataset to train and test the network and compare it with the existing up-sampling network, the experimental results show that PPSA-PU is better in terms of evaluation metrics and performance, and has better generalisation and robustness.

Early diagnosis of Alzheimer’s disease using a group self-calibrated coordinate attention network based on multimodal MRI

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer’s disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions.

Efficient urinary stone type prediction: a novel approach based on self-distillation

Urolithiasis is a leading urological disorder where accurate preoperative identification of stone types is critical for effective treatment. Deep learning has shown promise in classifying urolithiasis from CT images, yet faces challenges with model size and computational efficiency in real clinical settings. To address these challenges, we developed a non-invasive prediction approach for determining urinary stone types based on CT images. Through the refinement and improvement of the self-distillation architecture, coupled with the incorporation of feature fusion and the Coordinate Attention Module (CAM), we facilitated a more effective and thorough knowledge transfer. This method circumvents the extra computational expenses and performance reduction linked with model compression and removes the reliance on external teacher models, markedly enhancing the efficacy of lightweight models. achieved a classification accuracy of 74.96% on a proprietary dataset, outperforming current techniques. Furthermore, our method demonstrated superior performance and generalizability on two public datasets. This not only validates the effectiveness of our approach in classifying urinary stones but also showcases its potential in other medical image processing tasks. These results further reinforce the feasibility of our model for actual clinical deployment, potentially assisting healthcare professionals in devising more precise treatment plans and reducing patient discomfort.

Full-field displacement measurements of structural vibrations using a novel two-stage neural network

This paper presents a novel two-stage neural network for quantifying full-field displacements of vibrating structures. A graphics software was used to simulate structural vibrations with random displacement fields and generate image datasets through batch rendering. Subsequently, a two-stage neural network model comprising an object segmentation subnetwork for extracting structural shapes and an optical-flow subnetwork for identifying displacement fields was constructed. A coordinate convolution module was embedded in the optical-flow estimation model and the two subnetworks were integrated to develop a comprehensive model. An experiment was designed to monitor the vibration displacements in a cantilever column. The results showed that the proposed model achieved optimal recognition accuracy with a mean absolute error of 0.1477 pixels and root-mean-squared error of 0.2335 pixels. Additionally, it exhibited robustness to various lighting conditions. Furthermore, case studies on real-world structural vibration videos substantiated the practical engineering application value of the proposed model.

GRNet:Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling.

The lossy Geometry-based Point Cloud Compression (G-PCC) inevitably impairs the geometry information of point clouds, which deteriorates the quality of experience (QoE) in reconstruction and/or misleads decisions in tasks such as classification. To tackle it, this work proposes GRNet for the geometry restoration of G-PCC compressed large-scale point clouds. By analyzing the content characteristics of original and G-PCC compressed point clouds, we attribute the G-PCC distortion to two key factors: point vanishing and point displacement. Visible impairments on a point cloud are usually dominated by an individual factor or superimposed by both factors, which are determined by the density of the original point cloud. To this end, we employ two different models for coordinate reconstruction, termed Coordinate Expansion and Coordinate Refinement, to attack the point vanishing and displacement, respectively. In addition, 4-byte auxiliary density information is signaled in the bitstream to assist the selection of Coordinate Expansion, Coordinate Refinement, or their combination. Before being fed into the coordinate reconstruction module, the G-PCC compressed point cloud is first processed by a Feature Analysis Module for multiscale information fusion, in which kNN-based Transformer is leveraged at each scale to adaptively characterize neighborhood geometric dynamics for effective restoration. Following the common test conditions recommended in the MPEG standardization committee, GRNet significantly improves the G-PCC anchor and remarkably outperforms state-of-the-art methods on a great variety of point clouds (e.g., solid, dense, and sparse samples) both quantitatively and qualitatively. Meanwhile, GRNet runs fairly fast and uses a smaller-size model when compared with existing learning-based approaches, making it attractive to industry practitioners.

Analysis of Relay Coordination in Electrical Distribution Networks with Optimally Positioned Distributed Generation Units

As power demand increases and transmission and distribution capacity is limited, Distributed Generation (DG) units are gradually being incorporated into electrical power distribution networks. However, this integration may result in a significant impact on the coordination of protective relays, potentially generating issues such as blindness of protection and false tripping of overcurrent relays. To overcome these issues, an adaptive protection method is presented that adjusts relay sensitivity in response to changes in network design. The suggested technique dynamically modifies relay settings based on the line current, which fluctuates depending on the system design. Digital relays are needed for this method in order to save operational data in real time. Based on periodic observations, the Plug Multiplier Setting (PMS) and Time Multiplier Setting (TMS) parameters are optimized using a Fuzzy Inference System (FIS). Fuzzy rules are used by the adaptive algorithm for every relay. These rules are produced using the fuzzy editor using the inputs provided. An initial investigation was carried out to evaluate the effect of DG on the coordination of relay protection., as well as the optimal placement of DG to reduce losses and improve voltage profiles, using two base distribution networks. The algorithm was further tested on various configurations of IEEE distribution networks, and its effectiveness in achieving optimal relay coordination with adaptive settings was validated through the relay coordination module in ETAP.

A lightweight detection algorithm for tooth cracks in optical images

ObjectivesCracked tooth syndrome (CTS) is one of the major causes of tooth loss, presents the problem of early microcrack symptoms that are difficult to distinguish. This paper aims to investigate the practicality and feasibility of an improved object detection algorithm for automatically detecting cracks in dental optical images. MethodsA total of 286 teeth were obtained from Sun Yat-sen University and Guangdong University of Technology, and simulated cracks were generated using thermal expansion and contraction. Over 3000 images of cracked teeth were collected, including 360 real clinical images. To make the model more lightweight and better suited for deployment on embedded devices, this paper improves the YOLOv8 model for detecting tooth cracks through model pruning and backbone replacement. Additionally, the impact of image enhancement modules and coordinate attention modules on optimizing our model was analyzed. ResultsThrough experimental validation, we conclude that that model pruning reduction maintains performance better than replacing a lightweight backbone network on a tooth crack detection task. This approach achieved a reduction in parameters and GFLOPs by 16.8 % and 24.3 %, respectively, with minimal impact on performance. These results affirm the effectiveness of the proposed method in identifying and labeling tooth fractures. In addition, this paper demonstrated that the impact of image enhancement modules and coordinate attention mechanisms on YOLOv8's performance in the task of tooth crack detection was minimal. ConclusionsAn improved object detection algorithm has been proposed to reduce model parameters. This lightweight model is easier to deploy and holds potential for assisting dentists in identifying cracks on tooth surfaces.

White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism

White blood cells play a crucial role in the human immune system. The accurate classification of white blood cells can help doctors diagnose various diseases for patients. To enhance the classification accuracy of white blood cells micro-vision images, an efficient lightweight deep learning network called ICAFF-MobileNetv2 is proposed in this paper. Firstly, the pruning operations are applied to the MobileNetv2 and the multiscale feature extraction module is proposed to reduce the model size while ensuring the classification accuracy. Secondly, the improved coordinate attention mechanism module and the attention feature fusion module are incorporated into the inverted residual structures to enhance the feature extraction capability and the classification accuracy of the network. Furthermore, considering the imbalance among different types of white blood cells in the datasets, the method of label smoothing is introduced to improve the cross-entropy loss function and further enhance the classification accuracy. Finally, the improved network is validated on the proposed mixed dataset. The experimental results demonstrate that the proposed network features high classification accuracy and small model size. Compared to other classification networks, ICAFF-MobileNetv2 exhibits superior performance with 98.54 %, 98.21 %, 98.56 %, and 98.38 % of the classification accuracy, recall, precision and F1 score respectively, which are 0.7 %, 0.86 %, 0.92 %, and 0.89 % better than MobileNetv2.

Efficient Detection of Apparent Defects in Subway Tunnel Linings Based on Deep Learning Methods

High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the foundational architecture due to its comprehensive performance. The coordinate attention module and Bottleneck Transformer 3 were then integrated into the model’s backbone to enhance the focus on defect-prone areas and improve the learning of feature relationships between defects and other infrastructure. Subsequently, a high-resolution detection layer was added to the model’s head to further improve sensitivity to subtle defects. Additionally, a low-quality crack dataset was created using an open access dataset, and transfer learning combined with Real-ESRGAN was employed to enhance the detail and resolution of fine cracks. The results of the field experiments demonstrate that the proposed model significantly improves detection accuracy in high-incidence areas and for small-scale defects, achieving a mean average precision (mAP) of 87% in detecting cracks, leakage, exfoliation, and related infrastructure defects. Furthermore, the crack enhancement techniques substantially improve the representation of fine-crack details, increasing feature extraction accuracy by a factor of four. The findings of this paper could provide crucial technical support for the automated operation and maintenance of metro tunnels.

A Study of Improved Two-Stage Dual-Conv Coordinate Attention Model for Sound Event Detection and Localization.

Sound Event Detection and Localization (SELD) is a comprehensive task that aims to solve the subtasks of Sound Event Detection (SED) and Sound Source Localization (SSL) simultaneously. The task of SELD lies in the need to solve both sound recognition and spatial localization problems, and different categories of sound events may overlap in time and space, making it more difficult for the model to distinguish between different events occurring at the same time and to locate the sound source. In this study, the Dual-conv Coordinate Attention Module (DCAM) combines dual convolutional blocks and Coordinate Attention, and based on this, the network architecture based on the two-stage strategy is improved to form the SELD-oriented Two-Stage Dual-conv Coordinate Attention Model (TDCAM) for SELD. TDCAM draws on the concepts of Visual Geometry Group (VGG) networks and Coordinate Attention to effectively capture critical local information by focusing on the coordinate space information of the feature map and dealing with the relationship between the feature map channels to enhance the feature selection capability of the model. To address the limitation of a single-layer Bi-directional Gated Recurrent Unit (Bi-GRU) in the two-stage network in terms of timing processing, we add to the structure of the two-layer Bi-GRU and introduce the data enhancement techniques of the frequency mask and time mask to improve the modeling and generalization ability of the model for timing features. Through experimental validation on the TAU Spatial Sound Events 2019 development dataset, our approach significantly improves the performance of SELD compared to the two-stage network baseline model. Furthermore, the effectiveness of DCAM and the two-layer Bi-GRU structure is confirmed by performing ablation experiments.

Pipeline multi-type high consequence area identification based on mask R-CNN with fused attention mechanisms

Identification of high consequence areas is an important task in pipeline integrity management. However, traditional identification methods are generally characterized by low efficiency, high cost and low accuracy. For this reason, this paper proposes a recognition method based on the improved algorithm Mask Region-based Convolutional Neural Network. Coordinate attention mechanism module is introduced into the traditional Mask R-CNN algorithm to improve the recognition accuracy and reduce the training time. For the identification results, GIS tools are utilized to establish high consequence zones along both sides of the pipeline, and the grade and scope of the high consequence zones are determined according to relevant specifications.In this paper, this method is used to identify the high-consequence area of a pipeline section in Guangdong Province, the results show that: 1, the improved algorithm in the identification of densely populated, geologic hazards, flammable and explosive high consequence zones of the average accuracy of the identification of 1.7%, 3.4%, 3.9%. 2, The method in this paper identifies 8 more building elements and 0.311 more kilometers of pipeline mileage compared to traditional identification methods. The method of this paper can provide a reference for the early identification of high consequence areas of pipelines.

GPA-Net:No-Reference Point Cloud Quality Assessment With Multi-Task Graph Convolutional Network.

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shift, scaling, and rotation invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases.

Physicochemical Methods for the Structuring and Assembly of MOF Crystals.

ConspectusMetal-organic frameworks (MOFs) are promising for various applications through the creation of innovative materials and assemblies. This potential stems from their modular nature, as diverse metal ions and organic linkers can be combined to produce MOFs with unique chemical properties and lattice structures. Following extensive research on the design and postsynthetic chemical modification of MOF lattices at the molecular level, increasing attention is now focused on the next hierarchical level: controlling the morphology of MOF crystals and their subsequent assembly and positioning to create functional composites.Beyond well-established methods to regulate crystal size and shape through nucleation and coordination modulation, physicochemical techniques leveraging wetting effects, interparticle interactions, and magnetic or electric fields offer attractive avenues for the hierarchical structuring and assembly of MOFs. These techniques facilitate crystal alignment and yield unique superstructures. While our research group primarily focuses on directing MOF crystal orientation and positioning using external stimuli such as magnetic and electric fields, we also explore hierarchical MOF synthesis and structuring using liquid interfaces and depletion force-assisted packing.This account highlights our journey and progress in developing methods to regulate the morphology, assembly, orientation, and positioning of MOF crystals, placed in the context of work by other groups. First, we examine commonly utilized structuring methods for MOF crystals that employ liquid-liquid and air-liquid interfaces to spatially confine reactions, allowing us to access unique morphologies such as mushroom-like crystals and Janus particles. We also discuss strategies for concentrating and packing MOF crystals into superstructures, utilizing fluid interfaces for spatial confinement of crystals, depletion forces, entropic effects, and crystal sedimentation.A particularly compelling challenge in expanding the applicability of MOF materials is how to manipulate free-standing MOF crystals. This issue is especially important because MOFs are typically produced as loose powders, and industrial material processing is generally more efficient when the material is fluidized. While extensive research has been conducted regarding MOF growth on substrates with both positional and orientational control, there is a clear need for similar precision with free-standing MOFs dispersed in a fluid matrix. Our group has thus focused on the relatively new, yet powerful approach of using electric and magnetic fields to manipulate MOF crystals, which offers unprecedented control over the orientation and positioning of dispersed MOF crystals, complementing the more well-established methods of MOF growth on substrates. In this Account, we provide foundational background and discussions on the interactions between these external fields and MOF crystals, including critical considerations for effective MOF manipulation using such techniques. We also discuss their unique advantages and applications, and briefly examine potential application areas, such as photonics, smart materials like soft robotics and absorbents, and sensing. This Account highlights the promising potential of well-organized and aligned MOF crystals over randomly oriented ones in various applications, owing to enhanced selectivity and performance. It underscores the importance of specialized assembly methods to advance materials science and engineering, encouraging the reader to explore such approaches.

Research on strip surface defect detection based on improved YOLOv5 algorithm

In the production process of hot rolled strip, there will inevitably be defects such as iron scale, slag inclusion and scratch on the surface, and there is no effective identification method for these defects. Therefore, based on the improved YOLOv5s algorithm, this article proposes an improved YOLOv5s-GCB model by introducing the coordinate attention module, improving the feature fusion structure and lightening the feature extraction backbone network. The experiment shows that the precision of YOLOv5s-GCB is 89.7%, recall is 90.2% and mAP@0.5 is 93.1%, which are 2.1%, 3.0% and 1.6% higher than YOLOv5s, respectively. The average detection time of a single image is 7.9 ms, and the detection speed is improved. Finally, the ablation experiment shows that the proposed improved strategy is feasible, and the detection effect of YOLOv5s-GCB is further verified by the comparison experiment with other target detection models. Therefore, it can be concluded that the proposed method provides an important reference for the detection of indicated defects in strip steel.

STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels

Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors plays a crucial role in aiding physicians to improve diagnostic accuracy and treatment effectiveness. Nevertheless, inherent challenges associated with renal tumors, such as indistinct boundaries, morphological variations, and uncertainties in size and location, segmenting renal tumors accurately remains a significant challenge in the field of medical image segmentation. With the development of deep learning, substantial achievements have been made in the domain of medical image segmentation. However, existing models lack specificity in extracting features of renal tumors across different network hierarchies, which results in insufficient extraction of renal tumor features and subsequently affects the accuracy of renal tumor segmentation. To address this issue, we propose the Selective Kernel, Vision Transformer, and Coordinate Attention Enhanced U-Net (STC-UNet). This model aims to enhance feature extraction, adapting to the distinctive characteristics of renal tumors across various network levels. Specifically, the Selective Kernel modules are introduced in the shallow layers of the U-Net, where detailed features are more abundant. By selectively employing convolutional kernels of different scales, the model enhances its capability to extract detailed features of renal tumors across multiple scales. Subsequently, in the deeper layers of the network, where feature maps are smaller yet contain rich semantic information, the Vision Transformer modules are integrated in a non-patch manner. These assist the model in capturing long-range contextual information globally. Their non-patch implementation facilitates the capture of fine-grained features, thereby achieving collaborative enhancement of global–local information and ultimately strengthening the model’s extraction of semantic features of renal tumors. Finally, in the decoder segment, the Coordinate Attention modules embedding positional information are proposed aiming to enhance the model’s feature recovery and tumor region localization capabilities. Our model is validated on the KiTS19 dataset, and experimental results indicate that compared to the baseline model, STC-UNet shows improvements of 1.60%, 2.02%, 2.27%, 1.18%, 1.52%, and 1.35% in IoU, Dice, Accuracy, Precision, Recall, and F1-score, respectively. Furthermore, the experimental results demonstrate that the proposed STC-UNet method surpasses other advanced algorithms in both visual effectiveness and objective evaluation metrics.

Unet retinal vessel image segmentation algorithm based on coordinate attention mechanism and Min-ASPP module optimisation

In this paper, the Unet image segmentation algorithm is optimised and improved by introducing the coordinate attention mechanism and integrating the Min-ASPP module, and compared with the traditional Unet model, targeting the segmentation of retinal blood vessel images, which is a key problem in the field of medical image processing. The experimental results show that the optimised Unet algorithm combining the coordinate attention mechanism and Min-ASPP module has a smoother performance in the loss curve, faster convergence speed, and lower final converged loss value, which has an obvious advantage compared with the traditional Unet algorithm. Meanwhile, the optimised Unet algorithm shows higher accuracy and stability in the prediction results, providing ophthalmologists with more reliable information about the retinal vascular structure. Therefore, the research results of this paper show that the Unet image segmentation algorithm optimised by combining the coordinate attention mechanism and Min-ASPP module has better application prospects and effects in the retinal blood vessel image segmentation task, which provides a useful exploration and practice for improving the level of medical image processing technology.

DeformableFishNet: a high-precision lightweight target detector for underwater fish identification

The application of computer vision in fish identification facilitates researchers and managers to better comprehend and safeguard the aquatic ecological environment. Numerous researchers have harnessed deep learning methodologies for studying fish species identification. Nonetheless, this endeavor still encounters challenges such as high computational costs, a substantial number of parameters, and limited practicality. To address these issues, we propose a lightweight network architecture incorporating deformable convolutions, termed DeformableFishNet. Within DeformableFishNet, an efficient global coordinate attention module (EGCA) is introduced alongside a deformable convolution network (EDCN/EC2f), which is grounded in EGCA, to tackle the deformation of fish bodies induced by swimming motions. Additionally, an EC2f-based feature pyramid network (EDBFPN) and an efficient multi-scale decoupling head (EMSD Head) are proposed to extract multi-scale fish features within a lightweight framework. DeformableFishNet was deployed on our freshwater fish dataset, with experimental outcomes illustrating its efficacy, achieving a mean average precision (mAP) of 96.3%. The model comprises 1.7 million parameters and entails 4.7 billion floating-point operations (FLOPs). Furthermore, we validated DeformableFishNet on three public underwater datasets, yielding respective mAPs of 98%, 99.4%, and 83.6%. The experiments show that DeformableFishNet is suitable for underwater identification of various scenes.