Abstract
In order to further improve the accuracy and real-time performance of the traditional Single Shot Multibox Detector (SSD) object detection model, an improved SSD multi-object detection model is proposed. Firstly, aiming at the defect of weak correlation between prediction object score and positioning accuracy in the traditional SSD model, the improved model enhanced the correlation between the two by adding Intersection Over Union(IoU) prediction loss branch. Secondly, in order to reduce the spatial redundancy of traditional SSD model, a multi-frequency feature component convolution module is designed, which greatly reduces the calculation overhead and hardware overhead of the traditional model. Finally, in order to accelerate the convergence speed of the improved model, the Adaptive and Momental Bound (AdaMod) optimizer is introduced to modify the adaptive learning rate of the improved model which is too large in the training process. Experimental results show that the improved model has stronger detection capabilities, better overall detection results, and improved detection accuracy and real-time detection.
Highlights
With the continuous improvement and development of the object detection technology, there are more ideal use experience and a wide range of applications
It can be seen from table 1 that when the value of ρ is set to 1, the average accuracy value AP of the model is slightly increased by 1.4% compared with the original Single Shot Multibox Detector (SSD) algorithm, which indicates that IOU prediction loss branch of the improved SSD model is beneficial to improve model performance
The improved SSD multi-object detection model has been improved in terms of detection rate and efficiency, and reduced the calculation cost and related hardware cost of the model
Summary
With the continuous improvement and development of the object detection technology, there are more ideal use experience and a wide range of applications. The SSD algorithm uses the methods of generating default box and convolution prediction to achieve the purpose of multi-object detection by comprehensive utilization of the output feature maps of different convolution layers. In order to improve the detection accuracy and real-time performance of traditional SSD object detection algorithm, the model of this paper makes the following related work: Firstly, to enhance the correlation between object score and positioning accuracy, the IoU prediction loss branch was added to the improved model. Before outputting the prediction box of the object to be detected, the final score of the default box is calculated by equation (14), in which the parameter ρ is used to control the weight of the category score and the IoU value. Applying the calculation results to the ranking in the NMS process can better suppress the poor local detection
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