IFD: An Intelligent Fast Detection for Real-Time Image Information in Industrial IoT

Abstract

The processing of images by a convolutional neural network will lead to the loss of image information. Downsampling operation within the network is the main reason for the loss. To cut back the loss and reach an acceptable detection speed, this paper proposes an Intelligent Fast Detection for Real-time Image Information in Industrial IoT (IFD). IFD adopts the improved YOLO-Tiny framework and integrates the VaryBlock module. Firstly, we elect a tiny version of YOLO as the backbone and integrate the VaryBlock module into the network structure. Secondly, WGAN is applied to expand the training dataset of small objects. Finally, we use the unsupervised learning algorithm k-means++ to obtain the best-preset boundary box to improve the accuracy of the classification results. IFD optimizes the loss and detection accuracy of image information while meeting the detection speed. The MS-COCO dataset and RGB images in the TUM dataset are used for training and evaluating our model. The upgraded network’s average accuracy is around 8% higher than the YOLO-Tiny series network, according to the experimental data. The increased network’s detection speed in our hardware settings is at least 65 frames per second.

Keywords

• object detection
• YOLO-Tiny
• VaryBlock
• K-means++