Lightweight DL-based Drone Image Classification for Surveillance Applications

With the extensive use of surveillance-based systems in the present age, it is recommended to employ lightweight deep neural networks (DNNs) that not only have small silicon footprints and low latency but also provide enhanced security. In this paper, we incorporate a lightweight Attention-CNN algorithm on drones for image classification in smart city surveillance systems. By processing data directly on the drone, our approach significantly reduces the delay associated with data transmission to centralized servers, enhancing the effectiveness of surveillance operations. We ensure that our algorithms maintain high accuracy while operating within the constraints of the hardware resources. Additionally, we evaluate the resilience of these DL models against adversarial attacks, considering the security implications of deploying in real-life applications. To address the demands of latency-sensitive applications that require rapid analysis of high-resolution images, we also explore the implementation on a field-programmable gate array board. The experimental results show a maximum reduction factor of 9.56 in the memory size along with a 2.92% drop in the accuracy in the software implementation and the total power consumption on the ZCU104 FPGA board is 10 W.