engleski

Detection of Toy Soldiers Taken from a Bird’s Perspective Using Convolutional Neural Networks

This paper describes the use of two different deep-learning approaches for object detection to recognize a toy soldier. We use recordings of toy soldiers in different poses under different scenarios to simulate the appearance of persons on footage taken by drones. Recordings from a bird’s eye view are today widely used in the search for missing persons in non-urban areas, border control, animal movement control, and the like. We have compared the single-shot multi-box detector (SSD) with the MobileNet or Inception V2 as a backbone, SSDLite with MobileNet and Faster R- CNN combined with Inception V2 and ResNet50. The results show that Faster R-CNN detects small objects such as toy soldiers more successfully than SSD, and the training time of Faster R-CNN is much shorter than that of SSD.

Object detectors ; SSD ; Faster R-CNN

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