Results 11 to 20 of about 10,282,673 (296)
Small Object Detection with Multiscale Features [PDF]
International Journal of Digital Multimedia Broadcasting, 2018 The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image.
Guo X. Hu +4 more
doaj +4 more sources
SSDLiteX: Enhancing SSDLite for Small Object Detection
Applied Sciences, 2023 Object detection in many real applications requires the capability of detecting small objects in a system with limited resources. Convolutional neural networks (CNNs) show high performance in object detection, but they are not adequate to resource ...
Hyeong-Ju Kang
doaj +2 more sources
SensorsAerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution.
Jie Luo +5 more
doaj +2 more sources
R-CNN for Small Object Detection
, 2017 Existing object detection literature focuses on detecting a big object covering a large part of an image. The problem of detecting a small object covering a small part of an image is largely ignored. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images.
Chenyi Chen +3 more
openaire +2 more sources
Knowledge-Assisted Small Object Detection
Small Object Detection (SOD) is a challenging task due to the small size of objects and the complexity of noisy backgrounds, which are common in fields like surveillance and autonomous driving.
Le Hoang Duong +3 more
openaire +2 more sources
YOLO‐RSFM: An efficient road small object detection method
IET Image ProcessingTo tackle challenges in road multi‐object detection, such as object occlusion, small object detection, and multi‐scale object detection difficulties, a new YOLOv8n‐RSFM structure is proposed. The key improvement of this structure lies in the introduction
Pei Tang +4 more
doaj +2 more sources
A Small-Object-Detection Algorithm Based on LiDAR Point-Cloud Clustering for Autonomous Vehicles
Sensors3D object-detection based on LiDAR point clouds can help driverless vehicles detect obstacles. However, the existing point-cloud-based object-detection methods are generally ineffective in detecting small objects such as pedestrians and cyclists ...
Zhibing Duan +4 more
doaj +2 more sources
Dataset for small object detection with shadow (SODwS)
Data in BriefDetecting small objects in aerial images poses several challenges, including issues with resolution limitations, scale variability, background clutter, and object occlusion. Annotated datasets for small objects in aerial images are often scarce, complicating the training and validation of detection models.
Shahbe Mat-Desa +8 more
openaire +3 more sources
Improving small objects detection using transformer [PDF]
Journal of Visual Communication and Image Representation, 2021 General artificial intelligence is a trade-off between the inductive bias of an algorithm and its out-of-distribution generalization performance. The conspicuous impact of inductive bias is an unceasing trend of improved predictions in various problems in computer vision like object detection.
Shikha Dubey +3 more
openaire +4 more sources
Spatiotemporal tubelet feature aggregation and object linking for small object detection in videos [PDF]
, 2022 This paper addresses the problem of exploiting spatiotemporal information to improve small object detection precision in video. We propose a two-stage object detector called FANet based on short-term spatiotemporal feature aggregation and long-term ...
Mucientes Molina, Manuel Felipe +5 more
core +1 more source