BirdView Retina-Net: Small-Scale Object Detector for Unmanned Aerial Vehicles
2021 16th International Conference on Emerging Technologies (ICET)(2021)
摘要
Recent advancements in computer vision and deep learning techniques have led to many applications in robotics. These techniques play a significant part in the development of artificially intelligent autonomous and semi-autonomous robotic systems, e.g., Unmanned Aerial Vehicles (UAVs) and autonomous cars. Object detection and tracking is an important pillar for such robotic systems. For object detection, a wide variety of deep learning frameworks have been proposed over the past few years. However, objects in the video from a UAV are small and thus very hard to localize and classify. This research proposes a deep learning framework based object detection approach named as BirdView Retina-Net (BV-RNet). BV-RNet is an object detection framework capable of efficiently detecting small-scaled objects from an aerial view-point. BV-RNet extracts dense features and optimizes pre-defined anchors for inference to learn features effectively. VisDrone dataset is used to evaluate the performance of the framework. We have compared the results of BV-RNet with past VisDrone challenge competitors and other commonly employed past object detection frameworks. We have reported that our algorithm achieves mean average precision (mAP) of 31.8 on test-dev dataset, which is among the highest mAP achieved so far on VisDrone. Furthermore, it also outperforms several of the top class-wise mAP of various classes when compared with VisDrone past challenge competitors.
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关键词
computer vision,object detection,UAVs,Retina-Net
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