Unmanned surface vehicles (USVs) increasingly rely on vision-based perception for safe navigation and maritime surveillance, while onboard computing is constrained by strict size, weight, and power (SWaP) budgets. Although deep convolutional neural networks (CNNs) offer strong recognition performance, their computational and memory requirements pose significant challenges for deployment on low-cost embedded platforms. This paper presents a hardware–software co-design architecture and deployment study for CNN acceleration on a heterogeneous ARM–FPGA system, targeting energy-efficient near-sensor processing for embedded maritime applications. The proposed approach exploits a fully streaming hardware architecture in the FPGA fabric, based on line-buffered convolutions and AXI-Stream dataflow, while the ARM processing system is responsible for lightweight configuration, scheduling, and data movement. The architecture was evaluated using representative CNN models trained on a maritime ship dataset. Our experimental results on a Zynq-7020 system-on-chip demonstrate that the proposed co-design strategy achieves a balanced trade-off between throughput, resource utilisation, and power consumption under tight embedded constraints, highlighting its suitability as a practical building block for onboard perception in USVs.

Urban traffic accidents remain an important road-safety problem, especially in densely populated cities where traffic conditions can change quickly and increase the possibility of accidents. This study proposes a machine-learning framework to predict urban traffic accidents over a short time using traffic, weather, congestion, and route-condition information collected in Marrakech, Morocco. The dataset was collected at five-minute intervals over a period of approximately six months and contains about 890,000 traffic observations and 3980 recorded accident events. The proposed approach includes several steps, including data preprocessing, feature analysis, Principal Component Analysis (PCA), chronological data splitting, treatment of class imbalance, machine-learning classification, and performance evaluation. The dataset was divided chronologically into 65% for training, 10% for validation, and 25% for testing. The different sampling methods were applied only to the training data to avoid affecting the validation and testing sets. Four sampling strategies were looked at: Random Over-Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), Random Under-Sampling (RUS), and Cluster Centroids (CC). After that several traditional machine-learning classifiers were tested. Among the evaluated classifiers, XGBoost achieved the strongest predictive performance. The results show that ROS and SMOTE improve the detection of accident cases, whereas RUS and CC lead to comparatively higher classification errors. These results indicate that an XGBoost-based approach can be useful for predicting whether an accident will occur during the following five-minute interval. The proposed framework provides a basis for developing real-time intelligent traffic-safety systems. Future work will consider additional spatial, temporal, environmental, and behavioral features and will evaluate the framework using datasets from other urban areas.