Edge AI Drone: Lightweight MobileNetV3-SSD for Real-Time Detection of Abandoned Weapons in Outdoor Terrains

The growing need for rapid situational awareness in outdoor environments has highlighted the demand for lightweight, real-time hazard-detection systems deployable on unmanned aerial vehicles (UAVs). This study presents EdgeAI-Drone, a novel MobileNetV3-SSD–based framework optimized for real-time detection of abandoned weapons in natural terrains. A fully custom dataset of 2,350 images was developed using Philippine outdoor environments, capturing various weapon replicas under diverse lighting, terrain, and occlusion conditions. Images were manually annotated in Pascal VOC format and augmented with geometric and photometric transformations to enhance robustness. The proposed model was trained using transfer learning and optimized through structured pruning and INT8 quantization, enabling deployment on resource-constrained edge devices such as the NVIDIA Jetson Nano and Coral Edge TPU. Experimental results demonstrate that EdgeAI-Drone achieved high detection accuracy, with a Precision of 0.91, Recall of 0.94, F1-score of 0.92, mAP@0.5 of 0.87, and mAP@0.5:0.95 of 0.71. Real-time inference speeds were recorded at 22–24 FPS on the Jetson Nano and 55–60 FPS on the Coral Edge TPU. The system remained operationally robust across UAV flight altitudes of 5 m, 10 m, and 15 m, with graceful performance degradation at higher altitudes. Qualitative results further confirmed the model’s ability to identify partially occluded weapon replicas in cluttered outdoor settings. The findings indicate that integrating lightweight CNN architectures with edge-optimized deployment pipelines can enable practical, reliable UAV-based hazard detection systems. EdgeAI-Drone demonstrates strong potential for supporting search-and-rescue missions, post-conflict site assessments, border monitoring, and disaster response operations. Future work includes expanding to multi-class hazard detection, incorporating thermal/infrared sensing, and integrating autonomous UAV navigation for fully automated field hazard assessment.

Edge AI, UAV-based detection, MobileNetV3-SSD, Abandoned weapons, Object detection, Real-time inference, Embedded systems, Outdoor hazard detection, TensorRT optimization, Computer vision, Aerial imagery, Deep learning, Autonomous drones

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