Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic fea...
Crowding and occlusion pose significant challenges for pedestrian detection, which can easily lead to missed and false detections for small-scale and occluded pedestrian objects in dense pedestrian scenarios. To enhance dense pedestrian detection acc...
As one of the main disasters that limit the formation of wheat yield and affect the quality of wheat, lodging poses a great threat to safety production. Therefore, an improved PSPNet (Pyramid Scene Parsing Network) integrating the Normalization-based...
The pothole is a common road defect that seriously affects traffic efficiency and personal safety. Road evaluation and maintenance and automatic driving take pothole detection as their main research part. In the above scenarios, accuracy and real-tim...
In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability...
Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and...
Wavefront sensing is an essential technique in optical imaging, adaptive optics, and atmospheric turbulence correction. Traditional wavefront reconstruction methods, including the Gerchberg–Saxton (GS) algorithm and phase diversity (PD) techniq...
As machine vision technology has advanced, pig face recognition has gained wide attention as an individual pig identification method. This study establishes an improved ResNAM network as a backbone network for pig face image feature extraction by com...
Individual cow identification is a prerequisite for automatically monitoring behavior patterns, health status, and growth data of each cow, and can provide the assistance in selecting excellent cow individuals for breeding. Despite high recognition a...
To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Sp...
Steel surface defect detection is a crucial step in ensuring industrial production quality. However, due to significant variations in scale and irregular geometric morphology of steel surface defects, existing detection algorithms show notable defici...
Steel surface defect inspection plays an essential role in maintaining product quality and production safety in industrial manufacturing. However, existing detection methods still encounter difficulties in accurately identifying tiny defects, suppres...
Object detection has made tremendous progress in natural images over the last decade. However, the results are hardly satisfactory when the natural image object detection algorithm is directly applied to satellite images. This is due to the intrinsic...
Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme w...
Accurately detecting targets in remote-sensing images is crucial for the military, urban planning, and resource exploration. There are some challenges in extracting detailed features from remote-sensing images, such as complex backgrounds, large-scal...
Object detection in road scenes is a task that has recently become popular and it is also an important part of intelligent transportation systems. Due to the different locations of cameras in the road scenes, the size of the traffic objects captured...
The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techn...
Student behavior recognition in classroom environments is important for teaching quality assessment and intelligent education, yet it remains challenging due to dense student distributions, frequent occlusion, substantial scale variation, and the sub...
Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing th...
Capsicums (chili peppers), serving as critical economic crops, hold a significant role within greenhouse cultivation, driving the development of the agricultural economy. Traditional methods for detecting chili pepper flowers are notably time-consumi...
Heat stress severely impacts pig welfare and farm productivity. However, existing methods lack the capability to detect subtle physiological cues (e.g., skin erythema) in complex farm environments while maintaining real-time efficiency. This paper pr...
In intelligent building, efficient surface defect detection is crucial for structural safety and maintenance quality. Traditional methods face three challenges in complex scenarios: locating defect features accurately due to multi-scale texture and b...
Sleep, as the basis for regular body functioning, can affect human health. Poor sleep conditions can lead to various physical ailments, such as poor immunity, memory loss, slow cognitive development, and cardiovascular diseases. Along the increasing...
With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and produci...
Accurate counting of the number of rice panicles per unit area is essential for rice yield estimation. However, intensive planting, complex growth environments, and the overlapping of rice panicles and leaves in paddy fields pose significant challeng...
Bruising is a common occurrence in apples that can lead to gradual fruit decay and substantial economic losses. Due to the lack of visible external features, the detection of early-stage bruising (occurring within 0.5 h) is difficult. Moreover, the i...
To enhance the accuracy of agricultural area classification and enable remote sensing monitoring of agricultural regions, this paper investigates classification models and their application in change detection within rural areas, proposing the MC&...
Mechanical component detection in industrial scenes is challenged by cluttered backgrounds, large-scale variation, specular reflection, high inter-class similarity, and class imbalance. To address the above problems, this paper proposes a lightweight...
Tuna accounts for 20% of the output value of global marine capture fisheries, and it plays a crucial role in maintaining ecosystem stability, ensuring global food security, and supporting economic stability. However, improper management has led to si...
Well-facilitated farmland plays an important role in stabilizing and increasing crop yields, advancing agricultural mechanization, and improving production efficiency. Field-road quality directly affects machinery access and the transport of agricult...
In this study, we designed a multi-temperature synchronous prediction method for municipal solid waste incineration (MSWI) based on a non-stationary Crossformer model. It is mainly divided into the following steps: First, use a normalization module t...
Medical image segmentation based on deep learning is a central research issue in the field of computer vision. Many existing segmentation networks can achieve accurate segmentation using fewer data sets. However, they have disadvantages such as poor...
Traffic flow prediction is crucial in intelligent transportation systems. Considering the severe disruptions caused by traffic accidents or congestion, a time series model is developed for traffic flow prediction based on multiple random walks on gra...
Image inpainting aims to fill in corrupted regions with visually realistic and semantically plausible contents. In this paper, we propose a progressive image inpainting method, which is based on a forked-then-fused decoder network. A unit called PC-R...
Background/Objectives: Glaucoma is a leading cause of irreversible blindness worldwide, making accurate and efficient detection methods essential. One primary concern with glaucoma is that it often presents no early symptoms. Vision loss typically be...
In modern industrial production, unsupervised anomaly detection methods have gained significant attention due to their ability to address the challenge posed by the scarcity of labeled anomaly samples. Among them, unsupervised anomaly detection metho...
To address the challenges of detecting a large number of objects and a high proportion of small objects in aerial drone imagery, we proposed an aerial dense small object detection algorithm called Global Normalization Attention Mechanism You Only Loo...
Images captured under complex conditions frequently have low quality, and image performance obtained under low-light conditions is poor and does not satisfy subsequent engineering processing. The goal of low-light image enhancement is to restore low-...
Benefiting from the development of unmanned aerial vehicles (UAVs), the types and number of datasets available for image synthesis have greatly increased. Based on such abundant datasets, many types of virtual scenes can be created and visualized usi...
Tea disease detection is crucial for improving the agricultural circular economy. Deep learning-based methods have been widely applied to this task, and the main idea of these methods is to extract multiscale coarse features of diseases using the bac...
Background: Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading t...
Autonomous vehicles need to explain their actions to improve reliability and build user trust. This study focuses on enhancing the transparency and explainability of the decision-making process in such systems. A module named XAI-Interpreter is devel...
Traffic flow prediction faces challenges such as prediction difficulties caused by data non-stationarity and the high computational complexity of existing attention mechanisms. To overcome these challenges, this paper proposes a traffic flow predicti...
In order to realize the online diagnosis and prediction of gasoline engine fire faults, this paper proposes an improved misfire fault detection algorithm model based on YOLOv8 for sound signals of gasoline engines. The improvement involves substituti...
Normalization narrows visual domain gaps but moves discriminative cues into its residual. Many normalization-residual decomposition methods for domain generalization (DG) and unsupervised domain adaptation (UDA) rely primarily on channel-wise modelin...
To address the coexistence of multiple defect types and class imbalance in complex urban sewer scenarios, this study proposes a two-stage multi-label recognition method for sewer defect images. In the first stage, a lightweight binary convolutional n...
Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimi...
Background: Electroencephalography (EEG) signals play a crucial role in diagnosing epilepsy by reflecting distinct patterns associated with normal brain activity, ictal (seizure) states, and interictal (between-seizure) periods. However, the manual c...
The traditional Siamese object tracking algorithm uses a convolutional neural network as the backbone and has achieved good results in improving tracking precision. However, due to the lack of global information and the use of spatial and scale infor...
This paper presents an automatic classification system for pulmonary diseases in chest X-rays based on geometric normalization. The proposed method consists of three main modules. Module 1: A landmark detector: A ResNet-18 convolutional neural networ...
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