Abstract
Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.
Highlights
With the continuous improvement of deep learning related theories, computer vision technologies [1,2,3] have achieved great success
As the basis of computer vision tasks, object detection [4,5,6] has been applied in many fields such as intelligent security [7], automatic driving [8], and intelligent medical treatment [9]; even some industrial applications are based on object detection algorithms [10,11,12,13]
In the past few years, in order to improve the real-time performance and accuracy of object detection in complex scenes, many scholars have conducted a lot of research on this, and the object detection algorithms based on deep learning have achieved remarkable achievements
Summary
With the continuous improvement of deep learning related theories, computer vision technologies [1,2,3] have achieved great success. In the past few years, in order to improve the real-time performance and accuracy of object detection in complex scenes, many scholars have conducted a lot of research on this, and the object detection algorithms based on deep learning have achieved remarkable achievements. E highlight of this algorithm is to propose region proposal network (RPN) network structure, which combines region generation with convolution neural network based on the default box mechanism It further improves the real-time performance of the Fast RCNN algorithm and becomes the most representative algorithm in the two-stage detection algorithm. In order to improve the accuracy of the single-stage detection algorithm, Fu et al [27] proposed a feature fusion method for multiscale prediction based on the SSD algorithm and used deconvolution operation to enhance the semantic information of shallow features. Under the premise of real-time detection, the improved algorithm greatly improves the accuracy of small object detection in complex scenes
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