Communication Tower Recognition Using PaddlePaddle: An Industrial AI Practice

This article presents an industrial AI application for communication tower recognition using PaddlePaddle's PaddleDetection framework. Communication towers are essential infrastructure for wireless communication, with over a million towers deployed nationwide in China. Accurate real-time identification of these towers is crucial for maintenance and operations, involving billions of yuan in annual revenue and expenditure.

Technical Challenges:

Outdoor露天环境 with numerous interfering elements

High similarity between different tower categories

Environmental factors (rain, snow, glare, cloudy weather) affecting recognition accuracy

Solution Approach:

The project collaborated with China Tower Corporation to develop a solution using the PP-PicoDet model from PaddleDetection. The optimization strategies included:

Transfer learning with COCO pre-trained models

Loss function modification

Learning rate adjustment

Quantization training

Experimental Results (tested on Kirin 980 mobile device):

方案

模型

推理时间/ms

mAP0.5

1

PP-PicoDet(Baseline)

-

90.6%

2

PP-PicoDet+COCO预训练

125

94.7%

3

PP-PicoDet+COCO预训练+修改loss

-

94.5%

4

PP-PicoDet+COCO预训练+调小lr

-

94.7%

5

PP-PicoDet+COCO预训练+修改lr再训练

-

94.9%

6

PP-PicoDet+COCO预训练+量化

78

94.5%

Recommended Solution:

Scheme 6 (PP-PicoDet with COCO pre-training + quantization) achieves a balance between speed and accuracy, with mAP of 94.5% and inference time of 78ms.

Deployment:

The solution supports deployment via PaddleLite for high-performance edge computing, with ONNX export capability for MNN/NCNN/OpenVINO. Complete Android deployment demos are provided.