27 papers · ranked by Valyu relevance
Muhammad Ahmed, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki + 3 more
'Didier Stricker' 'Muhammad Zeshan Afzal' 'Radu Danescu'] Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of 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…
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…
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…
Peng Gu, Xiaosong Lan, Shuxiao Li, Hyoungsik Nam
When compared with the traditional manual design method, the convolutional neural network has the advantages of strong expressive ability and it is insensitive to scale, light, and deformation, so it has become the mainstream method in the object detection field. In order to further improve the accuracy of existing…
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…
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…
Nikhil Thakurdesai, Anupam Tripathi, Dheeraj Butani, Smita Sankhe
— Blind people face a lot of problems in their daily routines. They have to struggle a lot just to do their day-today chores. In this paper, we have proposed a system with the objective to help the visually impaired by providing audio aid guiding them to avoid obstacles, which will assist them to move in their…
Xiang Li, Yuan Tian, Fuyao Zhang, Shuxue Quan + 1 more
Figure 1: These images show the results of different object detection methods applied on the same image: (a) results of original DNN (SSD Mobilenet model), (b) DNN with image orientation correction, (c) DNN with image correction and scale-based filtering, (d) DNN with image correction and online semantic mapping, and…
Berat Mert Albaba, Sedat Özer
—Recent advances in camera equipped drone applications and their widespread use increased the demand on vision based object detection algorithms for aerial images. Object detection process is inherently a challenging task as a generic computer vision problem, however, since the use of object detection algorithms on…
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…
D Waithe, JM Brown, K Reglinski, I Diez-Sevilla + 2 more
In this paper we demonstrate the application of object detection networks for the classification and localization of cells in fluorescence microscopy. We benchmark two leading object detection algorithms across multiple challenging 2-D microscopy datasets as well as develop and demonstrate an algorithm which can…
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…
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…
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…
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…
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…
Susan G. Wardle, Beth Rispoli, Vinai Roopchansingh, Chris I. Baker
Humans are skilled at recognizing everyday objects from pictures, even if we have never encountered the depicted object in real life. But if we have encountered an object, how does that real-world experience affect the representation of its photographic image in the human brain? We developed a paradigm that involved…
J. Almeida, A. Fracasso, S. Kristensen, D. Valério + 4 more
Understanding how we recognize everyday objects requires unravelling the variables that govern the way we think about objects and the way in which our representations are organized neurally. A major hypothesis is that the organization of object knowledge follows key object-related dimensions, analogously to how sensory…
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…
Paul Morris, Cory Simon
In many gas sensing tasks, we simply wish to become aware of gas compositions that deviate from normal, "business-as-usual" conditions. We provide a methodology, illustrated by example, to computationally predict the performance of a gas sensor array design for detecting anomalous gas compositions. Specifically, we…