Deep Reinforcement Learning for Object Detection Based on Improved Reward Mechanism
To improve the detection accuracy and speed of deep reinforcement learning object detection models,modifications are made to traditional models.To address inadequate feature extraction,a VGG16 feature extraction module integrated with a channel attention mechanism is introduced as the state input for reinforcement learning,enabling a more comprehensive capture of key information in images.To address inaccurate evaluation caused by relying solely on the intersection over union as a reward,an improved reward mechanism that also considers the distance between the center points and the aspect ratio of the ground truth box and the predicted box is employed,making the reward more reasonable.To accelerate the convergence of the training process and enhance the objectivity of the agent's evaluation of current states and actions,the Dueling DQN algorithm is used for training.After conducting experiments on the PASCAL VOC2007 and PASCAL VOC2012 datasets,experimental results show that the detection model only needs 4-10 candidate boxes to detect the target.Compared with Caicedo-RL,the accuracy is improved by 9.8%,and the mean intersection over union between the predicted and ground truth boxes is increased by 5.6%.
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