Results 71 to 80 of about 7,363,707 (395)
A Survey of Object Detection for UAVs Based on Deep Learning
Remote Sensing, 2023 With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs.
Guangyi Tang +4 more
doaj +1 more source
Salient object detection via objectness measure [PDF]
2015 IEEE International Conference on Image Processing (ICIP), 2015 Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth ...
R Sai Srivatsa, R. Venkatesh Babu
openaire +2 more sources
Emerging Science Journal, 2022 A deep learning-based object detector has been successfully applied to all application areas. It has high immunity to variations in illumination and deviations among objects.
Wattanapong Kurdthongmee +2 more
doaj +1 more source
A MultiPath Network for Object Detection [PDF]
Procedings of the British Machine Vision Conference 2016, 2016 The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that ...
Zagoruyko, Sergey +6 more
openaire +4 more sources
A comprehensive review on intelligent surveillance systems
Communications in Science and Technology, 2016 Intelligent surveillance system (ISS) has received growing attention due to the increasing demand on security and safety. ISS is able to automatically analyze image, video, audio or other type of surveillance data without or with limited human ...
Sutrisno Warsono Ibrahim
doaj +1 more source
Detecting out-of-context objects using contextual cues [PDF]
IJCAI-ECAI 2022, 2022 This paper presents an approach to detect out-of-context (OOC) objects in an image. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the scene context and detect the OOC object with a bounding box. In this work, we consider commonly explored contextual relations such as co-occurrence relations, the ...
arxiv
Multiple kernels for object detection [PDF]
2009 IEEE 12th International Conference on Computer Vision, 2009 Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image sub-windows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential χ2 kernels, each of which captures a different feature channel ...
Vedaldi, A +4 more
openaire +3 more sources
Anchor pruning for object detection
Computer Vision and Image Understanding, 2022 This paper proposes anchor pruning for object detection in one-stage anchor-based detectors. While pruning techniques are widely used to reduce the computational cost of convolutional neural networks, they tend to focus on optimizing the backbone networks where often most computations are.
Maxim Bonnaerens +2 more
openaire +3 more sources
A Coarse to Fine Framework for Object Detection in High Resolution Image [PDF]
arXiv, 2023 Object detection is a fundamental problem in computer vision, aiming at locating and classifying objects in image. Although current devices can easily take very high-resolution images, current approaches of object detection seldom consider detecting tiny object or the large scale variance problem in high resolution images. In this paper, we introduce a
arxiv
Multi-view 3D Object Detection Network for Autonomous Driving [PDF]
Computer Vision and Pattern Recognition, 2016 This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes ...
Xiaozhi Chen +4 more
semanticscholar +1 more source