A Framework and Method for Surface Floating Object Detection Based on 6G Networks

Abstract

Water environment monitoring has always been an important method of water resource environmental protection. In practical applications, there are problems such as large water bodies, long monitoring periods, and large transmission and processing delays. Aiming at these problems, this paper proposes a framework and method for detecting floating objects on water based on the sixth-generation mobile network (6G). Using satellite remote sensing monitoring combined with ground-truth data, a regression model is established to invert various water parameters. Then, using chlorophyll as the main reference indicator, anomalies are detected, early warnings are given in a timely manner, and unmanned aerial vehicles (UAVs) are notified through 6G to detect targets in abnormal waters. The target detection method in this paper uses MobileNetV3 to replace the VGG16 network in the single-shot multi-box detector (SSD) to reduce the computational cost of the model and adapt to the computing resources of the UAV. The convolutional block attention module (CBAM) is adopted to enhance feature fusion. A small target data enhancement module is used to enhance the network identification capability in the training process, and the key-frame extraction module is applied to simplify the detection process. The network model is deployed in system-on-a-chip (SOC) using edge computing, the processing flow is optimized, and the image preprocessing module is added. Tested in an edge environment, the improved model has a 2.9% increase in detection accuracy and is 55% higher in detection speed compared with SSD. The experimental results show that this method can meet the real-time requirements of video surveillance target detection.

A lightweight method for maize seed defects identification based on Convolutional Block Attention Module

“…( Ma et al., 2019 ) proposed CBAM-GAN generative adversarial network, which can significantly improve the quality of generated images. ( Li et al., 2022 ) adopted MobileNetv3 to detect floating objects on the water surface and CBAM to enhance feature fusion. The experiment showed that the detection accuracy of the improved model increased by 2.9% and the detection speed increased by 55%.…”

Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment

“…To address the problem of small targets being easily missed and mistakenly detected in application scenarios, this paper proposes a data augmentation method, STDA (Small Target Data Augmentation, STDA), for small target training using the Mosaic algorithm. The STDA process [41] is shown in Figure 3. The procedure initiates with the fusion of four original images, followed by random scaling down and the application of data augmentation operations, such as flipping and merging.…”