Anchor-Free Object Detection with Scale-Aware Networks for Autonomous Driving
نویسندگان
چکیده
Current anchor-free object detectors do not rely on anchors and obtain comparable accuracy with anchor-based detectors. However, that adopt a single-level feature map lack pyramid network (FPN) prior information about an object’s scale; thus, they insufficiently adapt to large scale variation, especially for autonomous driving in complex road scenes. To address this problem, we propose divide-and-conquer solution attempt introduce some variation into the model when maintaining streamlined structure. Specifically, small-scale objects, add dense layer jump connections between shallow high-resolution layers deep high-semantic layers. For large-scale dilated convolution is used as ingredient cover features of objects. Based this, adaptation module proposed. In module, different expansion rates are utilized change network’s receptive field size, which can changes from large-scale. The experimental results show proposed has better detection performance scales than existing
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ژورنال
عنوان ژورنال: Electronics
سال: 2022
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics11203303