In power systems, the stability and reliability of transmission lines are crucial. Bolts, as key components for connecting and fixing the main body of the lines, play a decisive role in maintaining the stability of a power system. However, during the inspection of transmission lines, detecting defects in these bolts using vision-based methods becomes particularly difficult because of the small proportion, uneven distribution, and indistinct features of bolts in the inspection images. To address these issues, an adaptive block detection method consisting of two stages is designed. In the first stage, an improved target density distribution map generation network is employed to predict a target density distribution map containing the approximate size and distribution information of the targets. This network is composed of RepODconv convolution blocks based on parameter reconstruction and multidimensional dynamic convolution technology, which effectively controls the model's parameter quantity while enhancing the network's attention to small-sized targets. Subsequently, a clustering block algorithm is designed to obtain fixed-size and unscaled block-area images based on this target density distribution map. In the second stage, the YOLOX model combined with self-attention modules is employed to detect these images, enhancing the network's discrimination ability for defects of different categories. Experimental results on a dataset of transmission line bolt inspections by unmanned aerial vehicles show that the recall rate and precision of majority-class defects reach 70%. Compared to the experimental results of current advanced detection networks, the Average Precision at Intersection over Union (IoU) of 0.5 (mAP@0.5) is improved by approximately 30%, mean Average Precision (mAP) is improved by approximately 70%, and the mAP of small targets is improved by approximately 2 times.