26 papers · ranked by Valyu relevance
Arsalan Tahir, Hafiz Suliman Munawar, Junaid Akram, Muhammad Adil + 4 more
'Shehryar Ali' 'Abbas Z. Kouzani' 'M. A. Pervez Mahmud' 'Gemine Vivone'] Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various…
V Viswanatha, R K Chandana, A C Ramachandra
- The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This survey is all about YOLO and convolution neural networks (CNN) in the direction of real time object detection. YOLO does…
Nils Körber
In recent years the amount of data generated by imaging techniques has grown rapidly along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, we present the…
Qing Li, Shaopeng Hu, Kohei Shimasaki, Idaku Ishii + 2 more
'Krzysztof Bernacki' 'Mateja Novak'] This study proposes a visual tracking system that can detect and track multiple fast-moving appearance-varying targets simultaneously with 500 fps image processing. The system comprises a high-speed camera and a pan-tilt galvanometer system, which can rapidly generate large-scale…
Fazal Wahab, Inam Ullah, Anwar Shah, Rehan Ali Khan + 2 more
'Muhammad Shahid Anwar'] Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and…
Sukana Zulfqar, Sadia Saeed, M. Azam Zia, Anjum Ali + 2 more
Object detection in video and image surveillance is a well-established yet rapidly evolving task, strongly influenced by recent deep learning advancements. This review summarises modern techniques by examining architectural innovations, generative model integration, and the use of temporal information to enhance…
Prithwish Jana, Partha Pratim Mohanta
Object detection serves as a significant step in improving performance of complex downstream computer vision tasks. It has been extensively studied for many years now and current state-of-the-art 2D object detection techniques proffer superlative results even in complex images. In this chapter, we discuss the…
Sergio Paniego, Vinay Sharma, José María Cañas, Sylvain Girard
This paper introduces Detection Metrics, an open-source scientific software for the assessment of deep learning neural network models for visual object detection. This software provides objective performance metrics such as mean average precision and mean inference time. The most relevant international object detection…
Khalid Alnujaidi, Ghada Alhabib, Abdulaziz Alodhieb
As the population grows and more land is being used for urbanization, ecosystems are disrupted by our roads and cars. This expansion of infrastructure cuts through wildlife territories, leading to many instances of Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue that is having a global…
Md. Nuruzzaman Pranto, Omar Faruk
Efficient and accurate object detection is an important topic in the development of computer vision systems. With the advent of deep learning techniques, the accuracy of object detection has increased significantly. The project aims to integrate a modern technique for object detection with the aim of achieving high…
Shishir Muralidhara, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki + 3 more
'Marcus Liwicki' 'Didier Stricker' 'Muhammad Zeshan Afzal' 'Yitzhak Yitzhaky'] Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more…
Saqib Ali Nawaz, Jingbing Li, Uzair Aslam Bhatti, Muhammad Usman Shoukat + 1 more
'Muhammad Usman Shoukat' 'Raza Muhammad Ahmad'] Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years…
Benjamin Babaev, Saachi Goyal, Rachel A Ross
The estrous cycle regulates reproductive events and hormone changes in female mammals and is analogous to the menstrual cycle in humans. Monitoring this cycle is necessary as it serves as a biomarker for overall health and is crucial for interpreting study results. The estrous cycle comprises four stages influenced by…
Syed Muhammad Aamir, Hongbin Ma, Malak Abid Ali Khan, Muhammad Aaqib
Detection of small, undetermined moving objects or objects in an occluded environment with a cluttered background is the main problem of computer vision. This greatly affects the detection accuracy of deep learning models. To overcome these problems, we concentrate on deep learning models for real-time detection of…
Sucheng Kang
Compared with the traditional object detection algorithm, the object detection algorithm based on deep learning has stronger robustness to complex scenarios, which is the hot direction of current research. According to the process characteristics of the object detection algorithm based on deep learning, it is divided…
Imran Khan Mirani, Tianhua Chen, Malak Abid Ali Khan, Syed Muhammad Aamir + 1 more
'Syed Muhammad Aamir' 'Waseef Menhaj'] Imran Khan Mirani a , Chen Tianhuab , Malak Abid Ali Khanc , Syed Muhammad Aamirc , Waseef Menhajd a School of Information and Computer Engineering, Beijing University of Technology, Beijing, 100124, China b School of Artificial Intelligence, Beijing Technology & Business…
Michael A. Tabak, Daniel Falbel, Tess Hamzeh, Ryan K. Brook + 6 more
Motion-activated wildlife cameras, or camera traps, are widely used in biological monitoring of wildlife. Studies using camera traps amass large numbers of images and analyzing these images can be a large burden that inhibits research progress. We trained deep learning computer vision models using data for 168 species…
Malte Scherff, Markus Lappe
Optic flow, the retinal pattern of motion that is experience during self-motion contains information about one’s direction of heading. If the visual scene contains other moving objects optic flow becomes a combination of self-motion and independent object motion. The global pattern due to self-motion is locally…
Jeonghwan Cheon, Seungdae Baek, Se-Bum Paik
The ability to perceive visual objects with various types of transformations, such as rotation, translation, and scaling, is crucial for consistent object recognition. In machine learning, invariant object detection for a network is often implemented by augmentation with a massive number of training images, but the…
Jahid Chowdhury Choton, Venkat Margapuri, Ivan Grijalva, Brian J. Spiesman + 1 more
The performance of computer vision models for object detection and classification is heavily influenced by the number of classes and quality of input images, particularly in biological applications such as species-level identification of bumblebees. Bee identification is time-consuming, costly, and requires specialized…
Hongli Jiang, S. Hessam M. Mehr
The availability, portability, and low cost of electronic devices have made them a prime candidate for the rapid detection of chemical particles. Here we designed a chemical particle detection system based on a Raspberry Pi camera to detect micron droplets generated by ultrasonic atomizers. Through the analysis of…
Genevieve Jiawei Moat, Maxime Gaudet-Trafit, Julian Paul, Jaume Bacardit + 2 more
Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. To address this gap, we present MacqD, a modified Mask R-CNN model incorporating a SWIN transformer backbone for enhanced…
Heeseung Lee, Daeho Kim, Heyin Lee, Namyoung Gwak + 6 more
- 1. Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea - 2. Department of Materials Science and Engineering, Korea University, 145 Anam-ro, Seoul 02841, Republic of Korea - 3. Department of Chemical and Biological Engineering, Korea University, Seoul 02841…
Meng Jia, Troy Sorensen, Dorit Hammerling
We propose a generic, modular framework to optimize the placement of continuous monitoring sensors on oil and gas sites aiming to maximize the methane emissions detection efficiency. Our proposed framework substantially expands the problem scale compared to previous related studies and can be adapted for different…
Kanji Kaneko, Mamiko Tsugane, Taku Sato, Takeshi Hayakawa + 2 more
The detection of bioactive nanoparticles (NPs) plays an important role in the medical and diagnostic fields. Conventional techniques for the sensitive detection of target NPs must overcome challenges such as long processing time, complex sample preparation, and high cost. Here, we show that vibration-induced flow…
Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen + 3 more
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly…