用改进的小波散射网络实现无人机边缘端实时异物识别
Real-Time Foreign Object Recognition Based on Improved Wavelet Scattering Deep Network and Edge Computing
- 用小波散射替代传统卷积层，降低计算开销
- 在树莓派和Jetson Nano上实现720P图像<7ms推理，准确率超90%
- 比YOLOv5s高1.1%，适合电力巡检等边缘场景
新能源在电力系统中渗透率提升，对变电站和输电线路的运维提出更高要求。利用无人机实时识别异物可快速消除安全隐患。然而，受限于边缘设备算力，捕获图像难以本地实时处理。为此，提出一种基于改进小波散射深度网络的轻量级模型。该模型采用双正交小波基提取图像单通道的散射系数与模值系数，替代卷积神经网络中的卷积层和池化层；后续三个全连接层构成简化多层感知机（MLP）进行特征分类。实验表明，基于双正交小波基构建的模型可在树莓派和Jetson Nano等边缘设备上对720P（1280×720）图像实现识别分类，准确率高于90%，推理时间小于7ms。进一步实验显示，本模型识别准确率较YOLOv5s高1.1%，较YOLOv8s高0.3%。
原文摘要 · Abstract (English)
The increasing penetration rate of new energy in the power system has put forward higher requirements for the operation and maintenance of substations and transmission lines. Using the Unmanned Aerial Vehicles (UAV) to identify foreign object in real time can quickly and effectively eliminate potential safety hazards. However, due to the limited computation power, the captured image cannot be real-time processed on edge devices in UAV locally. To overcome this problem, a lightweight model based on an improved wavelet scatter deep network is proposed. This model contains improved wavelet scattering network for extracting the scatter coefficients and modulus coefficients of image single channel, replacing the role of convolutional layer and pooling layer in convolutional neural network. The following 3 fully connected layers, also constituted a simplified Multilayer Perceptron (MLP), are used to classify the extracted features. Experiments prove that the model constructed with biorthogonal wavelets basis is able to recognize and classify the foreign object in edge devices such as Raspberry Pi and Jetson Nano, with accuracy higher than 90% and inference time less than 7ms for 720P (1280*720) images. Further experiments demonstrate that the recognition accuracy of our model is 1.1% higher than YOLOv5s and 0.3% higher than YOLOv8s.
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