Power line detection method based on hybrid attention mechanism

Unmanned Aerial Vehicles (UAVs) play an important role in modern technology, especially in inspecting power lines and acquiring aerial imagery. Their flexibility and high adaptability make them ideal for these tasks. Although drones can capture large amounts of aerial image data, accurately detecting power lines within them is not easy. To solve the problem of power line detection, a novel solution is proposed: using a multi-scale U-shaped network model that incorporates a dual attention mechanism. Our design fully takes into account the complexity of the power line detection task and adopts a multi-level processing method to improve the accuracy of identification. The designed model includes an encoder and two decoders. The use of this structure helps to make fuller use of feature information. The encoder is responsible for converting the input image into a high-level abstract feature representation, while the decoder is responsible for mapping these features back to the original image space to achieve accurate localization and identification of power lines. In order to solve the problem of feature space information loss that may be caused by continuous downsampling, this model introduces two independent attention mechanisms, channel and space. This design can effectively compensate for the detailed information lost due to downsampling, thereby improving the ability to accurately extract power line boundaries. After a series of experiments, it has been proved that this method can achieve certain advantages.