Selection of Lightweight CNN Models with Limited Computing Resources for Drone Collision Prediction
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Abstract
The Collision Avoidance System (CAS) is a safety system created to identify and prevent collisions, primarily on drones. The CAS comprises three processes: detection, prediction, and action. The predictive process is crucial as it determines whether a collision will occur, making it the core component of the system. Most drones are equipped with cameras. A visual-based prediction involves the use of a convolutional neural network (CNN). The CNN operates by autonomously learning and extracting hierarchical characteristics from input data through convolution, pooling, and fully connected layers. Currently, there are CNN models called pretrained models that are ready to use. However, not all pretrained models are suitable for compatibility with drones as they possess computational constraints. Our objective is to establish a suitable model selection from a variety of pre-trained CNN models with lightweight architectures. The transfer learning technique is applied to customize these models with the ColANet dataset. Subsequently, we evaluate these models regarding their accuracy, model size, inference time, and power consumption. Finally, the selected model is deployed in real time on a Raspberry Pi 3B+ with data input from a DJI Tello drone camera, and the prediction performance is evaluated.
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