Abstract
In recent times, there has been a growing focus on research into bionic drones, which seek to mimic biological behavior and structure, thus overcoming the limitations of conventional drones. The ability of bionic drones to blend into their surroundings presents a significant challenge for identification. This study presents a dataset of bionic drones and introduces the Bionic Drone Identification Network (BDRNet). The dataset was enriched using data augmentation techniques to improve model recognition. Moreover, an Aggregated Attention Mechanism (AAM) captures input feature correlation. Furthermore, a Merged and Integrated Detector Head (MIDHead) and Multi-scale Lightweight Convolution (MLWConv) have been proposed to lessen computational costs. The findings reveal that BDRNet achieves an AP0.5 of 94.4 %, a Params of 2.6 M, and a Flops of 5.5G, surpassing mainstream object detection models such as Faster R-CNN and YoloV5. This suggests that BDRNet demonstrates robust recognition capabilities and holds potential for deployment in resource-constrained embedded devices.
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