Planning and scheduling for autonomous mobile robots using a deep learning method
Object detection has become critical in real-world applications, including autonomous driving, robotics, and quality control in manufacturing. This study presents the design and evaluation of a hybrid object detection pipeline that combines the strengths of YOLOV8 and Faster R-CNN to enhance dete...
محفوظ في:
المؤلف الرئيسي: | |
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مؤلفون آخرون: | |
التنسيق: | Final Year Project |
اللغة: | English |
منشور في: |
Nanyang Technological University
2024
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الموضوعات: | |
الوصول للمادة أونلاين: | https://hdl.handle.net/10356/181839 |
الوسوم: |
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الملخص: | Object detection has become critical in real-world applications, including autonomous
driving, robotics, and quality control in manufacturing. This study presents the design and
evaluation of a hybrid object detection pipeline that combines the strengths of YOLOV8 and
Faster R-CNN to enhance detection accuracy and confidence. The primary focus is to address
the limitations of individual models by leveraging their complementary strengths: YOLOV8's
real-time detection capabilities and Faster R-CNN's precision in refining low-confidence
predictions.
This study highlights the trade-offs between computational efficiency and detection accuracy,
as lower confidence thresholds captured more regions for refinement but increased
computational costs, while higher thresholds were computationally efficient but risked
missing opportunities to refine uncertain detections. The hybrid pipeline's flexibility was also
demonstrated, showing potential adaptability to other datasets or object detection tasks by
tuning thresholds and exploring alternative backbone architectures.
The proposed pipeline represents a step forward in combining lightweight real-time detectors
with accurate refinement models to achieve a balance between speed and precision. Future
work will focus on optimizing the pipeline for real-time deployment, expanding the dataset
for improved generalization, and exploring transformer-based architectures for further
performance gains. This study underscores the importance of model integration in tackling
complex object detection tasks and provides a solid foundation for developing advanced
hybrid systems. |
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