Real-time target detection method for computing resource limited platform deployment

Abstract

The invention discloses a real-time target detection method for computing resource limited platform deployment, and belongs to the field of deep learning and image processing. According to the methodthe YOLO-v3-tiny neural network is improved. The Tinier-YOLO reserves the first five convolution layers and pooling layers of the YOLO-v3-tiny and the prediction of two different scales, a Fire modulein the SqueezeNet is introduced, and a 1 * 1 bottleneck layer is connected with the Dense, so that the structure can be operated on an embedded AI platform in real time. The model size of the Tinier-YOLO provided by the invention is only 7.9 MB, and the real-time performance is improved by 21.8% compared with that of YOLO-v3-tiny, and is improved by 70.8% compared with that of YOLO-v2-tiny; compared with YOLO-v3-tiny, the accuracy of the method is improved by 10.1%, and compared with YOLO-v2-tiny, the accuracy of the method is improved by about 18.2%. According to the Tinier-YOLO provided bythe invention, the purpose of real-time detection still can be realized on a platform with limited computing resources, and the effect is better.

Description

technical field [0001] The invention relates to a real-time target detection method for platform deployment with limited computing resources, belonging to the fields of deep learning and image processing. Background technique [0002] Object detection is an important task in many emerging fields, such as robot navigation, autonomous driving, etc. In these complex scenes, object detection methods based on deep learning methods have greater advantages than traditional methods, and object detection algorithms based on deep learning continue to rise, such as R-CNN, SPPNet, fast-R-CNN, faster-R- CNN, R-FCN, and FPN. Although these object detection algorithms have achieved unprecedented accuracy, the detection speed is not fast, which is far from meeting the real-time requirements on devices with low computing power. At the same time, the size of deep learning models usually takes up a lot of storage space and requires powerful GPU computing capabilities. However, in most practi...

AI Technical Summary

Problems solved by technology

How to achieve real-time and accurate performance of YOLO on embedded and mobile devices, and realize real-time monitoring of multiple objects is still a major challenge

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