Many blockchain-based crowdsourcing frameworks currently struggle to address the high costs associated with on-chain storage and computation effectively, and they lack a quality-driven incentive mechanism tailored to bounding box annotation scenarios...
Ulcerative colitis is a chronic disease characterized by bleeding and ulcers in the colon. Disease severity assessment via colonoscopy videos is time-consuming and only focuses on the most severe lesions. Automated detection methods enable fine-grain...
In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection mod...
Manual pixel-level annotation remains a major bottleneck in deploying deep learning models for dense prediction and semantic segmentation tasks across domains. This challenge is especially pronounced in applications involving fine-scale structures, s...
AI-driven agricultural automation increasingly demands efficient data generation methods for training deep learning models in autonomous robotic systems. Traditional bounding box annotation methods for agricultural objects present significant challen...
A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of insta...
Most existing point cloud instance segmentation methods require accurate and dense point-level annotations, which are extremely laborious to collect. While incomplete and inexact supervision has been exploited to reduce labeling efforts, inaccurate s...
In the production and manufacturing industry, factors such as rolling equipment and processes may cause various defects on the surface of the steel plate, which greatly affect the performance and subsequent machining accuracy. Therefore, it is essent...
Colorectal cancer (CRC) is a leading cause of mortality worldwide, and preventive screening modalities such as colonoscopy have been shown to noticeably decrease CRC incidence and mortality. Improving colonoscopy quality remains a challenging task du...
Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs),...
Object Detection requires many precise annotations, which are available for natural images but not for many non-natural data sets such as artworks data sets. A solution is using Weakly Supervised Object Detection (WSOD) techniques that learn accurate...
This paper addresses the challenge of accurately estimating bee orientations on beehive landing boards, which is crucial for optimizing beekeeping practices and enhancing agricultural productivity. The research utilizes YOLOv8 pose models, trained on...
Object detection is a mature problem in autonomous driving, with pedestrian detection being one of the first commercially deployed algorithms. It has been extensively studied in the literature. However, object detection is relatively less explored fo...
This work presents a dataset comprising images, annotations, and velocity fields for benchmarking cell detection and cell tracking algorithms. The dataset includes two video sequences captured during laboratory experiments, showcasing the flow of red...
Vision-based vehicle smoke detection aims to locate the regions of vehicle smoke in video frames, which plays a vital role in intelligent surveillance. Existing methods mainly consider vehicle smoke detection as a problem of bounding-box-based detect...
Instance segmentation of sonar images is an effective method for underwater target recognition. However, the mismatch among positioning accuracy found by boxIoU and classification confidence, which is used as NMS score in current instance segmentatio...
Manual annotation of piglet imagery across varied farming environments is labor-intensive. To address this, we propose a semi-automatic approach within an active learning framework that integrates a pre-annotation model for piglet detection. We furth...
Video instance segmentation (VIS) is plagued by the high cost of pixel-level annotation and defects of weakly supervised segmentation, leading to the urgent need for a trade-off between annotation cost and performance. We propose a novel In-Depth Col...
Autonomous service robots assisting in homes and institutions should be able to store and retrieve items in household furniture. This paper presents a neural network-based computer vision method for detection of storage space within storage furniture...
Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately...
Thrombus imaging characteristics are associated with treatment success and functional outcomes in stroke patients. However, assessing these characteristics based on manual annotations is labor intensive and subject to observer bias. Therefore, we aim...
Why is there disparity in the miss rates of pedestrian detection between different age attributes? In this study, we propose to (i) improve the accuracy of pedestrian detection using our pre-trained model; and (ii) explore the causes of this disparit...
Facial analysis is an important area of research in computer vision and machine learning, with applications spanning security, healthcare, and user interaction systems. The data-centric AI approach emphasizes the importance of high-quality, diverse,...
This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it i...
We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each vid...
Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical decision-making, and potentially result in improved patient treatment. The...
Object detection and recognition in aerial and remote sensing images has become a hot topic in the field of computer vision in recent years. As these images are usually taken from a bird’s-eye view, the targets often have different shapes and a...
The dynamic development of deep learning methods in recent years has prompted the widespread application of these algorithms in the field of photogrammetry and remote sensing, especially in the areas of image recognition, classification, and object d...
Efficient and robust evaluation of kernel processing from corn silage is an important indicator to a farmer to determine the quality of their harvested crop. Current methods are cumbersome to conduct and take between hours to days. We present the ado...
Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performan...
Automated pig monitoring is important for smart pig farms; thus, several deep-learning-based pig monitoring techniques have been proposed recently. In applying automated pig monitoring techniques to real pig farms, however, practical issues such as d...
Recognizing plant leaves in complex agricultural scenes is challenging due to high manual annotation costs and real-time detection demands. Current deep learning methods, such as YOLOv8 and SAM, face trade-offs between annotation efficiency and infer...
We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no boun...
To improve the detection accuracy of the drone-based oriented vehicle object detection network and establish high-accuracy vehicle trajectory datasets, we present a freeway on-ramp vehicle (FRVehicle) detection dataset with oriented bounding box anno...
In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using H...
Recent outstanding results of supervised object detection in competitions and challenges are often associated with specific metrics and datasets. The evaluation of such methods applied in different contexts have increased the demand for annotated dat...
Arbitrary-oriented vehicle detection via aerial imagery is essential in remote sensing and computer vision, with various applications in traffic management, disaster monitoring, smart cities, etc. In the last decade, we have seen notable progress in...
The human facial bone is made up of many complex structures, which makes it challenging to accurately analyze fractures. To address this, we developed advanced image analysis software which segments and quantifies spaces between fractured bones in fa...
The steel manufacturing process is inherently continuous, meaning that if defects are not effectively detected in the initial stages, they may propagate through subsequent stages, resulting in high costs for corrections in the final product. Therefor...
As a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognitio...
Robust maritime obstacle detection is critical for safe navigation of autonomous boats and timely collision avoidance. The current state-of-the-art is based on deep segmentation networks trained on large datasets. However, per-pixel ground truth labe...
Detecting oriented small objects is a critical task in remote sensing, but the development of high-performance deep learning-based detectors is hindered by the need for large-scale and well-annotated datasets. The high cost of creating these datasets...
Space Surveillance and Tracking is a task that requires the development of systems that can accurately discriminate between natural and man-made objects that orbit around Earth. To manage the discrimination between these objects, it is required to an...
Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and metho...
Recent advances in foundation models have enabled strong zero-shot performance across vision tasks, yet their effectiveness for fine-grained domains such as food image segmentation remains underexplored. This study investigates the zero-shot capabili...
Annotations for image segmentation are expensive and time-consuming. In contrast to image segmentation, the task of object detection is in general easier in terms of the acquisition of labeled training data and the design of training models. In this...
Biofouling, the accumulation of marine organisms on submerged surfaces, presents significant operational challenges across various marine industries. Traditional detection methods are labor intensive and costly, necessitating the development of autom...
SMD (Singapore Maritime Dataset) is a public dataset with annotated videos, and it is almost unique in the training of deep neural networks (DNN) for the recognition of maritime objects. However, there are noisy labels and imprecisely located boundin...
This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation a...
SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 4...
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