A Target Detection Algorithm in Traffic Scenes Based on Deep
  Reinforcement Learning
Xinyu Ren Ruixuan Wang
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.