Transformer substation respirator color change detection method based on improved YOLOv8

The invention discloses an improved YOLOv8-based transformer substation respirator color change detection method, which introduces a BiFPN thought to improve a neck network of YOLOv8, and uses a more efficient multi-scale feature fusion mode to obtain multi-scale features with better representation capability. Meanwhile, a CBAM attention module is integrated in the method, the capacity of the model for extracting key features related to the respirator and defects of the respirator is enhanced, and the interference problem of the complex environment of a transformer substation on respirator color change detection is solved. According to the method, the YOLOv8 algorithm is applied to transformer substation respirator discoloration detection, and the respirator discoloration detection precision can be effectively improved while edge end deployment and real-time detection are met. 本发明公开了一种基于改进YOLOv8的变电站呼吸器变色检测方法，该方法引入BiFPN思想改进YOLOv8的颈部网络，使用更高效的多尺度特征融合方式获得更具表示能力的多尺度特征。同时，该方法还融入CBAM注意力模块，增强模型对呼吸器及其缺陷相关的关键特征提取能力，克服变