AI is employed in object detection to identify and locate objects within images or video. It utilizes deep learning techniques, such as convolutional neural networks (CNNs), to analyze visual data, detect objects of interest, and provide bounding box coordinates, enabling applications like autonomous driving, surveillance, and image recognition.
Researchers introduce NLE-YOLO, a novel low-light target detection network based on YOLOv5, featuring innovative preprocessing techniques and feature extraction modules. Through experiments on the Exdark dataset, NLE-YOLO demonstrates superior detection accuracy and performance, offering a promising solution for robust object identification in challenging low-light conditions.
Researchers introduce FulMAI, a cutting-edge system utilizing LiDAR, video tracking, and deep learning for accurate, markerless tracking and analysis of marmoset behavior. Achieving high accuracy and long-term monitoring capabilities, FulMAI offers valuable insights into marmoset behavior and facilitates research in brain function, development, and disease without causing stress to the animals.
Researchers proposed AutoGPT+P, a novel system for robotic task planning that merges an affordance-based scene representation with large language models (LLMs). By combining visual data-based scene affordance extraction with LLMs' capabilities, AutoGPT+P enables robots to understand natural language commands, perceive their environment, and generate plans to fulfill user requests, even in scenarios where required objects are absent.
This research presents YOLOv5s-ngn, a novel approach for air-to-air UAV detection addressing challenges in collision avoidance. Enhanced with lightweight feature extraction and fusion modules, alongside the EIoU loss function, YOLOv5s-ngn showcases superior accuracy and real-time performance, marking a significant advancement in vision-based target detection for unmanned aerial vehicles.
Chinese researchers introduce a novel approach, inspired by random forest, for constructing deep neural networks using fragmented images and ensemble learning. Demonstrating enhanced accuracy and stability on image classification datasets, the method offers a practical and efficient solution, reducing technical complexity and hardware requirements in deep learning applications.
Researchers present ReAInet, a novel vision model aligning with human brain activity based on non-invasive EEG recordings. The model, derived from the CORnet-S architecture, demonstrates higher similarity to human brain representations, improving adversarial robustness and capturing individual variability, thereby paving the way for more brain-like artificial intelligence systems in computer vision.
Chinese researchers introduce an innovative model utilizing computer vision and deep learning to recognize nine distinct behaviors of beef cattle in real-time. Enhancing the YOLOv8 algorithm with dynamic snake convolution and BiFormer attention mechanisms, the model achieves remarkable accuracy, demonstrating adaptability in various scenarios, including diverse lighting conditions and cattle densities.
Researchers introduce MFWD, a meticulously curated dataset capturing the growth of 28 weed species in maize and sorghum fields. This dataset, essential for computer vision in weed management, features high-resolution images, semantic and instance segmentation masks, and demonstrates promising results in multi-species classification, showcasing its potential for advancing automated weed detection and sustainable agriculture practices.
Scientists present a pioneering approach to address the scarcity of datasets for foreign object detection on railroad power transmission lines. The article introduces the RailFOD23 dataset, comprising 14,615 images synthesized through a combination of manual and AI-based methods, providing a valuable resource for developing and benchmarking artificial intelligence models in the critical domain of railway safety.
Researchers proposed a cost-effective solution to address the escalating issue of wildlife roadkill, focusing on Brazilian endangered species. Leveraging machine learning-based object detection, particularly You Only Look Once (YOLO)-based models, the study evaluated various architectures, introducing data augmentation and transfer learning to enhance model training with limited data.
Duke University researchers present a groundbreaking dataset of Above-Ground Storage Tanks (ASTs) using high-resolution aerial imagery from the USDA's National Agriculture Imagery Program. The dataset, with meticulous annotations and validation procedures, offers a valuable resource for diverse applications, including risk assessments, capacity estimations, and training object detection algorithms in the realm of remotely sensed imagery and ASTs.
Researchers present YOLO_Bolt, a lightweight variant of YOLOv5 tailored for industrial workpiece identification. With optimizations like ghost bottleneck convolutions and an asymptotic feature pyramid network, YOLO_Bolt outshines YOLOv5, achieving a 2.4% increase in mean average precision (mAP) on the MSCOCO dataset. Specialized for efficient bolt detection in factories, YOLO_Bolt offers improved detection accuracy while reducing model size, paving the way for enhanced quality assurance in industrial settings.
This paper presents an extensive dataset of approximately 11,000 stomatal images from temperate hardwood trees, covering 17 common species and 55 genotypes. The dataset, validated for accuracy and machine learning model training, enables high-throughput analysis of stomatal characteristics, exploration of diversity among tree types, and the creation of new indices for stomatal measurements, offering valuable insights for ecologists, plant biologists, and ecophysiologists.
Researchers introduce a groundbreaking Optical Tomography method employing Multi-Core Fiber-Optic Cell Rotation (MCF-OCR). This innovative system overcomes limitations in traditional optical tomography by utilizing an AI-driven reconstruction workflow, demonstrating superior accuracy in 3D reconstructions of live cells. The MCF-OCR system offers precise control over cell rotation, while the autonomous reconstruction workflow, powered by computer vision technologies, significantly enhances efficiency and accuracy in capturing detailed cellular morphology.
This article covers breakthroughs and innovations in natural language processing, computer vision, and data security. From addressing logical reasoning challenges with the discourse graph attention network to advancements in text classification using BERT models, lightweight mask detection in computer vision, sports analytics employing network graph theory, and data security through image steganography, the authors showcase the broad impact of AI across various domains.
Researchers present G-YOLOv5s-SS, a novel lightweight architecture based on YOLOv5 for efficient detection of sugarcane stem nodes. Achieving high accuracy (97.6% AP) with reduced model size, parameters, and FLOPs, this algorithm holds promise for advancing mechanized sugarcane cultivation, addressing challenges in seed cutting efficiency and offering potential applications in broader agricultural tasks.
The RefCap model pioneers visual-linguistic multi-modality in image captioning, incorporating user-specified object keywords. Comprising Visual Grounding, Referent Object Selection, and Image Captioning modules, the model demonstrates efficacy in producing tailored captions aligned with users' specific interests, validated across datasets like RefCOCO and COCO captioning.
Researchers introduced Swin-APT, a deep learning-based model for semantic segmentation and object detection in Intelligent Transportation Systems (ITSs). The model, incorporating a Swin-Transformer-based lightweight network and a multiscale adapter network, demonstrated superior performance in road segmentation and marking detection tasks, outperforming existing models on various datasets, including achieving a remarkable 91.2% mIoU on the BDD100K dataset.
Researchers proposed an IoT and ML-based approach to analyze ornamental goldfish behavior in response to environmental changes, particularly real-time water temperature and dissolved oxygen concentration. Utilizing IoT sensors and machine learning classifiers like Decision Tree, Naïve Bayes, Linear Discriminant Analysis, and K-Nearest Neighbor, the study demonstrated the effectiveness of the Decision Tree classifier in accurately classifying behavioral changes.
This article introduces an AI-based solution for real-time detection of safety helmets and face masks on municipal construction sites. The enhanced YOLOv5s model, leveraging ShuffleNetv2 and ECA mechanisms, demonstrates a 4.3% increase in mean Average Precision with significant resource savings. The study emphasizes the potential of AI-powered systems to improve worker safety, reduce accidents, and enhance efficiency in urban construction projects.
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