Open-world object detection (OWOD) focuses on training models with partially known class labels, enabling the detection of objects from known classes while concurrently identifying objects from unknown classes. Current models often perform suboptimal...
This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open...
In video object detection, the deterioration of an object’s appearance in a single frame brings challenges for recognition; therefore, it is natural to exploit temporal information to boost the robustness of video object detection. Existing met...
An advanced and user-friendly tool for fast labeling of moving objects captured with surveillance sensors is proposed, which is available to the public. This tool allows the creation of three kinds of labels: moving objects, shadows and occlusions. T...
This article explores the conditions that underlie the differential marking of objects in Spanish. It is argued here that the A-marker of the direct object is the realization of features in D (class and animate) and features in the verb (affectedness...
Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data,...
Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images...
Semi-supervised learning aims to improve the generalization performance of a model by exploiting the large quantity of unlabeled data together with limited labeled data during training. When applied to object detection in remote sensing images, semi-...
Semi-supervised training methods need reliable pseudo labels for unlabeled data. The current state-of-the-art methods based on pseudo labeling utilize only high-confidence predictions, whereas poor confidence predictions are discarded. This paper pre...
Currently, detectors have made significant progress in inference speed and accuracy. However, these detectors require Non-Maximum Suppression (NMS) during the post-processing stage to eliminate redundant boxes, which limits the optimization of model...
Inspection of the integrality of components and connecting parts is an important task to maintain safe and stable operation of transmission lines. In view of the fact that the scale difference of the auxiliary component in a connecting part is large...
Remote sensing of rotated objects often encounters numerous small and dense objects. To tackle small-object neglect and inaccurate angle predictions in elongated objects, we propose SA3Det, a novel method employing Pixel-Level Attention and Adaptive...
Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. For anchor-based detection models, the IoU (Intersection over Union) th...
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...
In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development...
In this paper, we introduce an efficient approach to multi-label image classification that is particularly suited for scenarios requiring rapid adaptation to new classes with minimal training data. Unlike conventional methods that rely solely on neur...
Object segmentation and object labeling are important techniques in the field of image processing. Because object segmentation techniques developed using two-dimensional images may cause segmentation errors for overlapping objects, this paper propose...
Artificial intelligence (AI) will play an important role in realizing maritime autonomous surface ships (MASSs). However, as a double-edged sword, this new technology brings forth new threats. The purpose of this study is to raise awareness among sta...
Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has attracted more and more attention because its training merely relies on image-level category labels, which significantly reduces the cost of manual annotation. With the exp...
The rapid development of Convolutional Neural Networks (CNNs) has markedly boosted the performance of object detection in remote sensing. Nevertheless, tiny objects typically account for an extremely small fraction of the total area in remote sensing...
Generative synthetic imagery can broaden visual coverage while still providing invalid or incomplete supervision to an object detector. This study revisits a failed synthetic-data pipeline for pile detection and tests whether severe localization-labe...
Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing...
To help consumers understand the healthfulness of food and make healthy food choices, manufacturers are increasingly providing front-of-package nutrition labels. However, not all types of front-of-package nutrition labels can promote consumers’...
Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Mi...
In the ongoing debate about front-of-package (FOP) nutrition labels, little data exist regarding nutritionally at-risk populations, although they are critical targets of prevention programs. This study aimed to compare the impact of FOP labels on the...
Deep learning-based object detection technology is rapidly developing, and underwater object detection, an important subcategory, plays a crucial role in various fields such as underwater structure repair and maintenance, as well as marine scientific...
Semi-supervised object detection helps to monitor and manage maritime transportation effectively, saving labeling costs. Currently, many semi-supervised object detection methods use a combination of data augmentation and pseudo-label to improve model...
Object detection is a crucial research topic in the fields of computer vision and artificial intelligence, involving the identification and classification of objects within images. Recent advancements in deep learning technologies, such as YOLO (You...
Although existing anchor-based oriented object detection methods have achieved remarkable results, they require manual preset boxes, which introduce additional hyper-parameters and calculations. These methods often use more complex architectures for...
Cloud detection in satellite imagery plays a pivotal role in achieving high-accuracy retrieval of biophysical parameters and subsequent remote sensing applications. Although numerous methods have been developed and operationally deployed, their accur...
Semi-supervised object detection (SSOD) has emerged as a promising technique to boost the performance of the detectors, utilizing both labeled and unlabeled data. However, in marine environments, SSOD faces formidable challenges posed by complex cond...
Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware S...
In this work, the creation of a dataset labeled in a pixel-wise manner for the uncommon domain of stain detection on patterned laundry is described. The unique properties of images in this dataset—stains are small and sometimes occur in large amounts...
Small pest detection in ultra-high-resolution forestry images is challenging due to extreme scale variation, complex backgrounds, and limited annotated data. To address these issues, we propose SSFPDet (Semi-Supervised Forest Pest Detector), a semi-s...
Object detection in unmanned aerial vehicle (UAV) aerial images has received extensive attention in recent years. The current mainstream oriented object detection methods for aerial images often suffer from complex network structures, slow inference...
The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant...
Behind the rapid development of deep learning methods, massive data annotations are indispensable yet quite expensive. Many active learning (AL) and semi-supervised learning (SSL) methods have been proposed to address this problem in image classifica...
Three-dimensional object detection based on the LiDAR point cloud plays an important role in autonomous driving. The point cloud distribution of the object varies greatly at different distances, observation angles, and occlusion levels. Besides, diff...
Object detection on aerial and satellite imagery is an important tool for image analysis in remote sensing and has many areas of application. As modern object detectors require accurate annotations for training, manual and labor-intensive labeling is...
Deformations introduced during the production of plastic components degrade the accuracy of their 3D geometric information, a critical aspect of object inspection processes. This phenomenon is prevalent among primary plastic products from manufacture...
Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In objec...
Reliable stored-grain pest monitoring is essential for smart granaries, yet probe-based field images pose three coupled bottlenecks: tiny and densely distributed pests in complex backgrounds, costly bounding-box annotation, and limited edge-side comp...
Secure grasping of objects in complex scenes is the foundation of many tasks. It is important for robots to autonomously determine the optimal grasp based on visual information, which requires reasoning about the stacking relationship of objects and...
Surface defect detection is essential for industrial quality control, but obtaining reliable labeled data remains costly due to the need for expert annotation. Semi-supervised object detection (SSOD) mitigates this need by leveraging unlabeled data t...
Recent advances in deep learning have significantly improved remote sensing object detection (RSOD). However, tiny object detection (TOD) remains challenging due to two main issues: (1) limited appearance cues and (2) the traditional Intersection ove...
This paper presents a supervised learning scheme that employs key-frame extraction to enhance the performance of pre-trained deep learning models for object detection in surveillance videos. Developing supervised deep learning models requires a signi...
Unsupervised domain adaptation (UDA) effectively transfers knowledge learned from a labeled source domain to an unlabeled target domain. The teacher–student framework, which generates pseudo-labels for target domain samples and uses them for ps...
Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potenti...
Open-world object detection (OWOD) aims to build detectors that can recognize known categories while simultaneously identifying unknown objects and incrementally learning novel classes. Despite recent advances, existing OWOD approaches still struggle...
The incidence of overweight and obesity has generated significant concerns among European consumers and institutions. As part of a set of measures undertaken, the European Union (EU) called for one harmonized mandatory front-of-pack nutritional label...
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