Academic Journal
Lightweight Substation Equipment Defect Detection Algorithm for Small Targets
| Title: | Lightweight Substation Equipment Defect Detection Algorithm for Small Targets |
|---|---|
| Authors: | Jianqiang Wang, Yiwei Sun, Ying Lin, Ke Zhang |
| Source: | Sensors, Vol 24, Iss 18, p 5914 (2024) |
| Publisher Information: | MDPI AG, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Chemical technology |
| Subject Terms: | defect detection, deep learning, substation equipment, small object detection, lightweight, YOLOv8, Chemical technology, TP1-1185 |
| More Details: | Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model’s feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model’s ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 24185914 1424-8220 |
| Relation: | https://www.mdpi.com/1424-8220/24/18/5914; https://doaj.org/toc/1424-8220 |
| DOI: | 10.3390/s24185914 |
| Access URL: | https://doaj.org/article/10c696c4468c4cb98041e17dcd344353 |
| Accession Number: | edsdoj.10c696c4468c4cb98041e17dcd344353 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 24185914 14248220 |
|---|---|
| DOI: | 10.3390/s24185914 |
| Published in: | Sensors |
| Language: | English |