The dramatic increase in the amount of garbage and complex diversity of the materials in the garbage bring serious environmental pollution problems and wastes resources. Recycling reduces waste but manual pipeline waste sorting involves a harsh working environment at high labor intensity with low sorting efficiency. In our paper, a novel intelligent garbage classification system based on deep learning and an embedded Linux system is proposed. The system is divided into three parts. First, a Raspberry Pi 4B is utilized as the master board for the hardware system. The peripherals of the system consist of a touch panel, sensors, a 2-DOF (degree of freedom) servo, and a camera. Second, a new GNet model for garbage classification based on transfer learning and the improved MobileNetV3 model is proposed. Third, a GUI based on Python and QT is employed to build a human-computer interaction system to facilitate system manipulation and observation. A series of garbage classification experiments on the Huawei Garbage Classification Challenge Cup dataset were conducted. The proposed classification system’s prediction accuracy was 92.62% at 0.63 s efficiency. The experimental results in this paper demonstrate that the proposed intelligent garbage classification system delivers high performance both in terms of accuracy and efficiency.

Video surveillance is an effective tool for traffic management and safety, but it may face challenges in extreme weather, low visibility, areas outside the monitoring field of view, or during nighttime conditions. Therefore, abnormal sound detection is used in traffic management and safety as an auxiliary tool to complement video surveillance. In this paper, a novel lightweight method for abnormal sound detection based on 1D CNN and Multi-Head Self-Attention Mechanism on the embedded system is proposed, which is named HADNet. First, 1D CNN is employed for local feature extraction, which minimizes information loss from the audio signal during time-frequency conversion and reduces computational complexity. Second, the proposed block based on Multi-Head Self-Attention Mechanism not only effectively mitigates the issue of disappearing gradients, but also enhances detection accuracy. Finally, the joint loss function is employed to detect abnormal audio. This choice helps address issues related to unbalanced training data and class overlap, thereby improving model performance on imbalanced datasets. The proposed HADNet method was evaluated on the MIVIA Road Events and UrbanSound8K datasets. The results demonstrate that the proposed method for abnormal audio detection on embedded systems achieves high accuracy of 99.6% and an efficient detection time of 0.06 s. This approach proves to be robust and suitable for practical applications in traffic management and safety. By addressing the challenges posed by traditional video surveillance methods, HADNet offers a valuable and complementary solution for enhancing safety measures in diverse traffic conditions.

In the context of the Internet of Things (IoT) and connected and automated vehicles (CAVs), eco-driving has become a key strategy for promoting sustainable and intelligent transportation by leveraging vehicle-to-everything (V2X) communication to optimize driving behavior, reduce energy consumption, and minimize environmental impact. However, achieving a dynamic balance among energy efficiency, traffic flow, safety, and driving comfort remains challenging in complex and congested urban environments such as roundabouts. This article proposes an IoT-enabled multiobjective eco-driving framework, termed deep deterministic policy gradient with Kolmogorov–Arnold networks (DDPG-KAN), which integrates the deep deterministic policy gradient (DDPG) algorithm with Kolmogorov–Arnold Networks (KANs) to enhance nonlinear feature representation and policy learning capability under mixed-traffic conditions. A multiobjective reward function is designed to jointly optimize safety, energy efficiency, traffic smoothness, and driving comfort, while an action inspector ensures collision avoidance, and a model predictive control (MPC) guarantees smooth and stable control execution. Leveraging IoT-based connectivity, the framework allows cooperative perception and adaptive decision-making among CAVs in real time. Simulation results show that the DDPG-KAN approach reduces energy consumption by 31.87%, improves traffic efficiency by 10.93%, decreases carbon emissions by 31.86%, lowers collision warnings by 38.34%, and enhances driving comfort by 53.46% compared to rule-based methods. These findings demonstrate the potential of DDPG-KAN as an effective IoT-driven solution for achieving low-carbon, safe, and comfortable mobility in intelligent connected vehicle systems, contributing to the realization of sustainable and green transportation networks.