Abstract
Abstract The problem of garbage suspension in transmission lines is an important factor affecting the reliability of the power grid. With the application of artificial intelligence in the detection of abnormal objects in transmission lines, the detection efficiency, accuracy, and stability have been improved to a certain extent. However, the accuracy of detecting suspended garbage in transmission lines is relatively poor. This article uses an improved YOLOv8 algorithm for suspended garbage detection, replacing the original loss function with the SIoU loss function, which improves the iteration speed and detection progress of the model. In addition, the use of depthwise separable convolution instead of the original dilated convolution compensates for the problem of information loss when size objects may exist, greatly improving the model’s ability and accuracy in identifying suspended garbage quality and laying the foundation for improving the accuracy of garbage detection in transmission lines.
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