A Target Detection Algorithm in Traffic Scenes Based on Deep Reinforcement Learning
This work addresses traffic scene analysis for applications like autonomous driving, but it appears incremental as it applies known deep reinforcement learning techniques to a specific domain.
The paper tackles traffic object detection by proposing a deep reinforcement learning model that uses a deep Q-network with LSTM-CNN to identify and align target zones, achieving exceptional precision in detecting traffic signal lights and speed limit signs in real-world scenarios.
This research presents a novel active detection model utilizing deep reinforcement learning to accurately detect traffic objects in real-world scenarios. The model employs a deep Q-network based on LSTM-CNN that identifies and aligns target zones with specific categories of traffic objects through implementing a top-down approach with efficient feature extraction of the environment. The model integrates historical and current actions and observations to make a comprehensive analysis. The design of the state space and reward function takes into account the impact of time steps to enable the model to complete the task in fewer steps. Tests conducted demonstrate the model's proficiency, exhibiting exceptional precision and performance in locating traffic signal lights and speed limit signs. The findings of this study highlight the efficacy and potential of the deep reinforcement learning-based active detection model in traffic-related applications, underscoring its robust detection abilities and promising performance.