The stability of transmission lines is a crucial guarantee for the normal operation of the power grid. To prevent accidents caused by accidental contact with conductors during line construction, this paper proposes a feature extraction network based on a multi-branch dual attention mechanism, DAMF-NET, addressing the low accuracy and poor reliability of existing detection methods. This algorithm enhances the network's focus on local features of target information by constructing a multi-branch dual attention mechanism, optimizing the feature extraction process. A multi-branch lightweight feature fusion network is proposed to reinforce the global multi-scale semantic information and feature significance under dense tasks, thereby improving the completeness of image features. A small object detection network is introduced to mitigate network scale variance and enhance the sensitivity of small object detection. By employing focal loss and EIoU optimized loss functions, the method reduces noise generated by positive and negative sample imbalance, accelerating the convergence speed of model training. Finally, a state recognition algorithm based on risk area localization is designed and deployed in the intelligent detection system of construction machinery. Experiments show that this method has better average precision compared to most current detection models, indicating its research significance in the detection of construction machinery and intelligent inspection.