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基于改進(jìn)YOLOv7的變電設(shè)備紅外圖像輕量識(shí)別檢測(cè)方法

來源：電工電氣發(fā)布時(shí)間：2024-12-02 09:02 瀏覽次數(shù)：36

基于改進(jìn)YOLOv7的變電設(shè)備紅外圖像輕量識(shí)別檢測(cè)方法

陳海波，葉金翔，王生祺

(國(guó)網(wǎng)浙江省電力有限公司超高壓分公司，浙江杭州 310000)

摘要：變電站設(shè)備準(zhǔn)確的紅外熱圖像識(shí)別與檢測(cè)是其溫度狀態(tài)智能分析的先決條件。為了克服復(fù)雜背景的干擾，提出了改進(jìn)的輕量級(jí) YOLOv7 方法，以提高在復(fù)雜紅外背景下變電站設(shè)備的識(shí)別效果。提出的方法引入了高分辨率 P2 檢測(cè)頭來改進(jìn)小目標(biāo)檢測(cè)，無參數(shù)注意模塊 SimAM 在復(fù)雜紅外背景中更好地提取不同變電設(shè)備目標(biāo)特征，CARAFE 模塊在上采樣過程中減少特征信息的損失，進(jìn)一步增強(qiáng)算法的魯棒性。實(shí)驗(yàn)及測(cè)試結(jié)果顯示提出的模型比原始 YOLOv7-tiny 高出 2.6% 檢測(cè)精度，實(shí)現(xiàn)了 101 FPS(幀數(shù))的實(shí)時(shí)推理速度，證明了所提出的模型在變電站設(shè)備的紅外圖像目標(biāo)識(shí)別方面的優(yōu)勢(shì)，特別是較小的變電設(shè)備，并且提出的模型比其他輕量級(jí)模型擁有更高的識(shí)別檢測(cè)精度。

關(guān)鍵詞： 變電設(shè)備；紅外圖像；目標(biāo)識(shí)別與檢測(cè)；計(jì)算機(jī)視覺；深度學(xué)習(xí)

中圖分類號(hào)：TM63 ；TP391.41 文獻(xiàn)標(biāo)識(shí)碼：B 文章編號(hào)：1007-3175(2024)11-0055-06

Lightweight Recognition and Detection Method for Infrared Images of

Substation Equipment Based on Improved YOLOv7

CHEN Hai-bo, YE Jin-xiang, WANG Sheng-qi

（State Grid Zhejiang Electric Power Co., Ltd. Ultra High Voltage Branch, Hangzhou 310000, China）

Abstract: The accurate recognition and detection of infrared thermal images of substation equipment is a prerequisite for intelligent analysis of its thermal status. To address the interference posed by complex backgrounds, this paper presents an improved light weight YOLOv7 method aimed at enhancing the recognition performance of substation equipment under intricate infrared conditions. The proposed approach introduces a high-resolution P2 detection head to improve small target detection, the parameter-free attention module SimAM effectively extracts target features of various substation equipment under the complex infrared backgrounds. Additionally, the CARAFE module minimizes the loss of feature information during the upsampling process, further bolstering the algorithm's robustness. Experimental results demonstrates that the proposed model surpasses the original YOLOv7-tiny by 2.6% in detection accuracy, achieving a real-time inference speed of 101 FPS. It is proved that the proposed model has advantages in infrared image target recognition of substation equipment, especially small substation equipment, and the proposed model has higher recognition and detection accuracy than other lightweight models.

Key words: substation equipment; infrared image; target recognition and detection; computer vision; deep learning

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基于改進(jìn)YOLOv7的變電設(shè)備紅外圖像輕量識(shí)別檢測(cè)方法