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Embedded flight devices with visual capabilities have become essential for a wide range of applications.
In aerial image detection, while many existing methods have partially addressed the issue of small target detection, challenges remain in optimizing small target detection and balancing detection accuracy with efficiency.
These issues are key obstacles to the advancement of real-time aerial image detection.
In this paper, we propose a new family of real-time detectors for aerial image detection, named FBRT-YOLO, to address the imbalance between detection accuracy and efficiency. Our method comprises two lightweight modules: Feature Complementary Mapping Module (FCM) and Multi-Kernel Perception Unit (MKP), designed to enhance object perception for small targets in aerial images.
FCM focuses on alleviating the problem of information imbalance caused by the loss of small target information in deep networks. It aims to integrate spatial positional information of targets more deeply into the network, better aligning with semantic information in the deeper layers to improve the localization of small targets.
We introduce MKP, which leverages convolutions with kernels of different sizes to enhance the relationships between targets of various scales and improve the perception of targets at different scales.
Extensive experimental results on three major aerial image datasets, including Visdrone, UAVDT, and AI-TOD, demonstrate that FBRT-YOLO outperforms various real-time detectors in terms of performance and speed.
Academic Publication FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection
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FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection
Embedded flight devices with visual capabilities have become essential for a wide range of applications. In aerial image detection, while many existing methods have partially addressed the issue o...
SOD-YOLO: Small-Object-Detection Algorithm Based on Improved YOLOv8 for UAV Images
The rapid development of unmanned aerial vehicle (UAV) technology has contributed to the increasing sophistication of UAV-based object-detection systems, which are now extensively utilized in civil...
YOLOv1 to YOLOv10: The Fastest and Most Accurate Real-time Object Detection Systems
This is a comprehensive review of the YOLO series of systems. Different from previous literature surveys, this review article reexamines the characteristics of the YOLO series from the latest techn...
YOLOv8n-FEL: A Lightweight Visual Detector with a Progressive Compress-Then-Enhance Architecture for Autonomous Driving
Real-time visual object detection on resource-constrained in-vehicle platforms requires a practical balance among detection accuracy, model size, computational complexity, and inference speed. Exis...
YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series
Abstract This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse...
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What is the core focus of the research titled 'FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection'?
This literature focuses on: Embedded flight devices with visual capabilities have become essential for a wide range of applications. In aerial image detection, while many existing methods have partially addressed the issue of small target detection, challenges remain in opt...
Are there open-source GitHub repositories related to FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection?
Yes, open-source projects like BigPizzaV3/CodexPlusPlus (An enhanced tool for CodexApp, striving to make Codex better to use and more comfortable 一个CodexApp的增强工具，努力让Codex变得更好用更舒服) are actively building upon these concepts.
Which startups are commercializing the technology behind FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection?
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What other academic literature is closely related to 'FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection'?
Yes, highly correlated activity was mapped. An entry titled 'FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detection' discusses this: Embedded flight devices with visual capabilities have become essential for a wide range of applications. In aerial image detection, while many exi...
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GitHubBigPizzaV3/CodexPlusPlus
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GitHubshepherd-agents/shepherd
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