Publications by Daniel Rossi

Automated plant species identification from citizen-science imagery is an established, demanding fine-grained recognition problem: large taxonomic label spaces, visually similar species, and long-tailed observations require real model capacity, while field use constrains memory, latency, and power. Model size is only part of the deployment cost: intermediate activations held in memory during inference and platformdependent execution behavior matter too, so compact recognition must be assessed on target hardware rather than through complexity metrics alone. We present BoltNet, an ultra-lightweight fully convolutional architecture combining a Spatial Redistribution Bottleneck and Logit PreSampling to improve the tradeoff between predictive performance and model size in high-cardinality classification, and report the AccuracyCompression Tradeoff as a complementary diagnostic. On Pl@ntNet300K, BoltNet reaches 0.682 F1-score with 341K parameters (1.37 MB), the highest F1-score among evaluated models below 2 MB and close to substantially larger convolutional backbones. Model-only measurements on a Raspberry Pi 5, Jetson Orin Nano, and Hailo-8 characterize execution across CPU, GPU, and NPU platforms, where BoltNet is the most consistently efficient model, with the best FPS/W on the GPU and NPU and second-best on the CPU. Results on AIDERv2 and CLRS provide secondary evidence of transfer across environmental image-classification tasks. Code available at: https://codeberg.org/danielrossi/BoltNet.

In this work, we present TakuNet, a family of ultra-lightweight convolutional neural networks designed for realtime aerial image classification on resource-constrained embedded devices. The proposed TakuNetV2 architecture enhances feature extraction and generalization capabilities through the incorporation of a denser stem coupled with hybrid feature extractor blocks, wherein diverse convolutional operations are synergistically combined to yield richer spatial representations without compromising latency or parameter efficiency. We extensively evaluate the TakuNet family on three public aerial image classification datasets against well-known light-weight and ultra-lightweight architectures, and measured relative performance on five heterogeneous embedded platforms, spanning from CPUs to GPUs, and the Hailo-8 NPU. TakuNet achieves state-of-the-art accuracy and energy efficiency, outperforming competing models in frames per second per additional watt consumed, confirming its suitability for battery-powered edge devices. Additionally, this paper introduces the Astrial platform, highlighting its role in enabling efficient deep learning inference on industrial-grade edge applications. Although current NPU hardware and compiler limitations pose challenges, TakuNet sets a new benchmark for efficient, high-performance embedded artificial intelligence in aerial surveillance and emergency response. Code, models, and weights are publicly available https://github.com/DanielRossi1/TakuNetV2.

Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses. In this work, we introduce TakuNet, a novel light-weight architecture which employs techniques such as depth-wise convolutions and an early downsampling stem to reduce computational complexity while maintaining high accuracy. It leverages dense connections for fast convergence during training and uses 16-bit floating-point precision for optimization on embedded hardware accelerators. Experimental evaluation on two public datasets shows that TakuNet achieves near-state-of-the-art accuracy in classifying aerial images of emergency situations, despite its minimal parameter count. Real-world tests on embedded devices, namely Jetson Orin Nano and Raspberry Pi, confirm TakuNet's efficiency, achieving more than 650 fps on the 15W Jetson board, making it suitable for real-time AI processing on resource-constrained platforms and advancing the applicability of drones in emergency scenarios. The code and implementation details are publicly released.