Power inspection in low-illuminance environments is of great significance for ensuring the all-weather stable operation of the power system. However, low visibility at night seriously interferes with the detection performance of small-sized power devices. In response to the issue, we propose a small object real-time detection method for power line inspection in low-illuminance environments. We design an adaptive transformer-ISP (ATISP) module, in which the optimal parameter regression module generates hyperparameters by sensing input image features to guide the image signal processors (ISPs) to perform image enhancement. With the advantage of ISPs, the ATISP has the advantages of fast inference speed and less training cost. Furthermore, the optimal parameter regression module extracts local features and long-distance dependencies through CNN and Transformer to be able to more fully perceive the input image, so that the generated hyperparameters better enhance image defects. In addition, we use lightweight neural network MobileNetv3 to improve YOLOv7, so that the algorithm maintains excellent small object detection performance while significantly increasing the detection speed. Moreover, the integrated model optimisation uses only the object detection loss functions, which allows ATISP to perform image enhancement just according to the object detection needs, improving small object detection effect and shortening the inference time of ATISP. In extensive experiments, compared with 9 state-of-the-art object detection algorithms, our algorithm has the best small-scale insulator faults detection precision (mAP:75.38$\%$) in our DIFE, best small object detection precision (mAP:56.31$\%$) in public dataset Exdark, and faster detection speed (FPS:98.81 and 97.53), which prove our method can achieve fast and accurate low-illuminance insulators detection.