SOD-YOLO: A lightweight small object detection framework. [PDF]
Sci RepCurrently, lightweight small object detection algorithms for unmanned aerial vehicles (UAVs) often employ group convolutions, resulting in high Memory Access Cost (MAC) and rendering them unsuitable for edge devices that rely on parallel computing. To address this issue, we propose the SOD-YOLO model based on YOLOv7, which incorporates a DSDM-LFIM ...
Xiao Y, Di N.
europepmc +5 more sources
Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images. [PDF]
Sensors (Basel)Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data.
Luo J +5 more
europepmc +5 more sources
SOD-YOLOv8-Enhancing YOLOv8 for Small Object Detection in Aerial Imagery and Traffic Scenes. [PDF]
Sensors (Basel)Object detection, as a crucial aspect of computer vision, plays a vital role in traffic management, emergency response, autonomous vehicles, and smart cities.
Khalili B, Smyth AW.
europepmc +4 more sources
Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection [PDF]
International Conference on Information Photonics, 2022 Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using ...
Fatih Çağatay Akyön +2 more
openalex +3 more sources
Augmentation for small object detection [PDF]
9th International Conference on Advances in Computing and Information Technology (ACITY 2019), 2019 In recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO.
Kisantal, Mate +4 more
openaire +3 more sources
Perceptual Generative Adversarial Networks for Small Object Detection [PDF]
Computer Vision and Pattern Recognition, 2017 Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the
Feng, Jiashi +5 more
core +2 more sources
Improving Small Object Proposals for Company Logo Detection [PDF]
Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, 2017 Many modern approaches for object detection are two-staged pipelines. The first stage identifies regions of interest which are then classified in the second stage.
Bell S. +6 more
core +4 more sources
A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images. [PDF]
Sci RepDetecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation.
Zhou S, Zhou H, Qian L.
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A small object detection model in aerial images based on CPDD-YOLOv8. [PDF]
Sci RepAerial images can cover a wide area and capture rich scene information. These images are often taken from a high altitude and contain many small objects.
Wang J, Gao J, Zhang B.
europepmc +2 more sources
SED-YOLO based multi-scale attention for small object detection in remote sensing. [PDF]
Sci RepObject detection is crucial for remote sensing image processing, yet the detection of small objects remains highly challenging due to factors such as image noise and cluttered backgrounds.
Wei X, Li Z, Wang Y.
europepmc +2 more sources