The uncontrolled spread of forest fires, driven by climate change and increasing human activities, poses a significant global challenge, disrupting the delicate balance of ecosystems. These fires, whether caused by natural disasters or human factors, have seve…
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4 results for “YOLOv4”
Accurate and efficient detection of aircraft at airports is crucial for enhancing airport security, ensuring safe operations, and improving overall air traffic management. As airports grow in complexity and traffic density increases, automated object detection…
Robust object detection and classification in dynamic industrial scenarios is still a challenge, in particular if the system has to work in real-time on mid-ranged hardware. Classical vision-based methods have difficulties to cope with low/weak illumination, r…
This study evaluates the performance of Faster R-CNN and YOLOv7 object detectors for dynamic ship detection in maritime applications. Both algorithms are tested under challenging conditions, including poor image quality due to cloud and dust obscuration, varyi…