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This research presents a novel active detection model utilizing deepreinforcement learning to accurately detect traffic objects in real-worldscenarios. The model employs a deep Q-network based on LSTM-CNN that identifiesand aligns target zones with specific categories of traffic objects throughimplementing a top-down approach with efficient feature extraction of theenvironment. The model integrates historical and current actions andobservations to make a comprehensive analysis. The design of the state spaceand reward function takes into account the impact of time steps to enable themodel to complete the task in fewer steps. Tests conducted demonstrate themodel's proficiency, exhibiting exceptional precision and performance inlocating traffic signal lights and speed limit signs. The findings of thisstudy highlight the efficacy and potential of the deep reinforcementlearning-based active detection model in traffic-related applications,underscoring its robust detection abilities and promising performance.

A Target Detection Algorithm in Traffic Scenes Based on Deep Reinforcement Learning