It is a common paradigm in object detection frameworks that the samples in training and testing have consistent distributions for the two main tasks: Classification and bounding box regression. This paradigm is popular in sampling strategy for traini...
Loss functions, such as the IoU Loss function and the GIoU (Generalized Intersection over Union) Loss function have been put forward to replace regression loss functions commonly used in regression loss calculation. GIoU Loss alleviates the vanishing...
Bounding box regression is a crucial step in object detection, directly affecting the localization performance of the detected objects. Especially in small object detection, an excellent bounding box regression loss can significantly alleviate the pr...
Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) repre...
Deep-learning-based object detectors have substantially improved state-of-the-art object detection in remote sensing images in terms of precision and degree of automation. Nevertheless, the large variation of the object scales makes it difficult to a...
In optical remote sensing image object detection, discontinuous boundaries often limit detection accuracy, particularly at high Intersection over Union (IoU) thresholds. This paper addresses this issue by proposing the Spatial Adaptive Angle-Aware (S...
Detecting small targets in infrared imagery remains highly challenging due to sub-pixel target sizes, low signal-to-noise ratios, and complex background clutter. This paper proposes PSHNet, a hybrid deep-learning framework that combines dense spatial...
The objective of detection in remote sensing images is to determine the location and category of all targets in these images. The anchor based methods are the most prevalent deep learning based methods, and still have some problems that need to be ad...
Object detection in aerial images has received extensive attention in recent years. The current mainstream anchor-based methods directly divide the training samples into positives and negatives according to the intersection-over-unit (IoU) of the pre...
Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localizati...
In synthetic aperture radar (SAR) images, ship targets are characterized by varying scales, large aspect ratios, dense arrangements, and arbitrary orientations. Current horizontal and rotation detectors fail to accurately recognize and locate ships d...
We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects: (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semant...
Oriented object detection (OOD) can more accurately locate objects with an arbitrary direction in remote sensing images (RSIs) compared to horizontal object detection. The most commonly used bounding box regression (BBR) loss in OOD is smooth L1 loss...
The accurate identification of pulmonary nodules is critical for the early diagnosis of lung diseases; however, this task remains challenging due to inadequate feature representation and limited localization sensitivity. Current methodologies often u...
Accurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Pr...
Land use/land cover (LULC) change has been recognized as one of the most important indicators to study ecological and environmental changes. Remote sensing provides an effective way to map and monitor LULC change in real time and for large areas. How...
To address the challenges associated with supervising workers who wear safety belts while working at heights, this study proposes a solution involving the utilization of an object detection model to replace manual supervision. A novel object detectio...
Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming pow...
In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which...
In this research, E2YOLOX-VFL is proposed as a novel approach to address the challenges of optical image multi-scale ship detection and recognition in complex maritime and land backgrounds. Firstly, the typical anchor-free network YOLOX is utilized a...
Due to the dense distribution of tomato fruit with similar morphologies and colors, it is difficult to recognize the maturity stages when the tomato fruit is harvested. In this study, a tomato maturity recognition model, YOLOv5s-tomato, is proposed b...
Object tracking has gained importance in various applications especially in traffic monitoring, surveillance and security, people tracking, etc. Previous methods of multiobject tracking (MOT) carry out detections and perform object tracking. Although...
Lifting hooks equipped with safety latches are critical terminal components of lifting machinery. The safety condition of this component is a crucial factor in preventing load dislodgement during lifting operations. To achieve intelligent monitoring...
Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect d...
This study employs unmanned aerial vehicles (UAVs) to detect mouse holes in grasslands, offering an effective tool for grassland ecological conservation. We introduce the specially designed CGT-YOLOv5n model, addressing long-standing challenges UAVs...
Underwater object detection is highly complex and requires a high speed and accuracy. In this paper, an underwater target detection model based on YOLOv8 (SPSM-YOLOv8) is proposed. It solves the problems of high computational complexities, slow detec...
Trackers based on the Siamese network have received much attention in recent years, owing to its remarkable performance, and the task of object tracking is to predict the location of the target in current frame. However, during the tracking process,...
In subway tunnel construction, current hand–eye integrated drilling robots use a camera mounted on the drilling arm for image acquisition. However, dust interference and long-distance operation cause a decline in image quality, affecting the st...
Background/Objectives: Sarcopenia, characterized by progressive loss of skeletal muscle mass and strength, significantly impacts physical function and quality of life in older adults. Traditional measurement methods like Dual-energy X-ray absorptiome...
In-field in situ droplet deposition digitization is beneficial for obtaining feedback on spraying performance and precise spray control, the cost-effectiveness of the measurement system is crucial to its scalable application. However, the limitations...
The object detection technologies of remote sensing are widely used in various fields, such as environmental monitoring, geological disaster investigation, urban planning, and military defense. However, the detection algorithms lack the robustness to...
Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look...
To address challenges like manual processes, complicated detection methods, high false alarm rates, and frequent errors in identifying defects on steel surfaces, this research presents an innovative detection system, YOLOv10n-SFDC. The study focuses...
In this paper, we propose an anchor-free smoke and fire detection network, ADFireNet, based on deformable convolution. The proposed ADFireNet network is composed of three parts: The backbone network is responsible for feature extraction of input imag...
28 September 2022
Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across dom...
Landslide detection is crucial for natural disaster risk management. Deep-learning-based object-detection algorithms have been shown to be effective in landslide studies. However, advanced algorithms currently used for landslide detection require hig...
In the intelligent perception of the marine engine room, visual identification of auxiliary equipment is the prerequisite for defect recognition and anomaly detection. To improve the detection accuracy, this study presents an auxiliary equipment dete...
Computer vision in marine ranching enables real-time monitoring of underwater resources. Detecting fish presents challenges due to varying water turbidity and lighting, affecting color consistency. We propose a Foreground Region Convolutional Neural...
Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring meth...
Due to target diversity, life-cycle variations, and complex backgrounds, traditional pest detection methods often struggle with accuracy and efficiency. This study introduces RDW-YOLO, an improved pest detection algorithm based on YOLO11, featuring t...
The automated detection and classification of immature macauba (Acrocomia aculeata) fruits is critical for improving post-harvest processing and quality control. In this study, we present a comparative evaluation of two state-of-the-art YOLO architec...
Quantification of renal perfusion based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires determination of signal intensity time courses in the region of renal parenchyma. Thus, selection of voxels representing the kidney mus...
Recently, ship detection in synthetic aperture radar (SAR) images has received extensive attention. Most of the current ship detectors preset dense anchor boxes to achieve spatial alignment with ground-truth (GT) objects. Then, the detector defines t...
Paper-based printing materials originate from the wood-based value chain–wood–pulp–paper–printing—and their yield reflects the utilization efficiency of pulp and paper resources. In roll-to-roll printing production, smal...
Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional meta...
Small targets in long-distance aerial photography have the problems of small size and blurry appearance, and traditional object detection algorithms face great challenges in the field of small-object detection. With the collection of massive data in...
Detecting small objects (e.g., manhole covers, license plates, and roadside milestones) in urban images is a long-standing challenge mainly due to the scale of small object and background clutter. Although convolution neural network (CNN)-based metho...
Accurate detection of wood surface defects plays a pivotal role in enhancing wood grade sorting precision, maintaining high standards in wood processing quality, and safeguarding forest resources. This paper introduces an efficient and precise approa...
CNN-based object detectors have achieved great success in recent years. The available detectors adopted horizontal bounding boxes to locate various objects. However, in some unique scenarios, objects such as buildings and vehicles in aerial images ma...
Ships are important targets for marine surveillance in both military and civilian domains. Since the rise of deep learning, ship detection in synthetic aperture radar (SAR) images has achieved significant progress. However, the variability in ship si...
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