目的 螺栓销钉是输电线路中至关重要的连接部件，螺栓的销钉缺失会导致输电线路中关键部件解体，甚至造成大规模停电事故。螺栓缺销检测属于小目标检测问题，由于其尺寸较小且背景复杂，现有的目标检测算法针对螺栓缺销的检测效果较差。为了提升输电线路中螺栓缺销的检测效果，本文以SSD （single shot multibox detector）算法为基础，提出了基于隔级交叉自适应特征融合的输电线路螺栓缺销检测方法。方法 在建立了螺栓缺销故障检测数据集后，首先在SSD网络中加入隔级交叉特征金字塔结构，增强特征图的视觉信息和语义信息；其次，引入自适应特征融合机制进行特征图二次融合，不同尺度的特征图以自适应学习到的权重进行加权特征融合，有效提升螺栓缺销的检测效果；最后，对原始的SSD网络中的先验框尺寸进行调整，使其大小和长宽比更加适合螺栓目标。结果 实验结果表明，本文方法在正常螺栓类的检测精度达到87.93%，螺栓缺销类的检测精度达到89.15%。与原始的SSD网络相比，检测精度分别提升了2.71%和3.99%。结论 本文方法针对螺栓缺销故障的检测精度较高，较原始SSD网络的检测精度有明显提升，与其他方法相比也有一定优势。为后续进一步提升螺栓缺销的检测精度以及对输电线路中其他部件的识别检测工作奠定了良好的基础。

Objective Bolts are widely distributed connecting components in transmission lines for maintaining the safe and stable operation. Bolt-relevant pins loss may threaten to the key components disintegration for transmission lines and even cause large-scale power outages. To eliminate potential safety hazards and ensure the safe and stable operation of the line, it is inevitable to resolve missing-pins bolt issues timely and accurately. Traditional manual-based transmission line inspection has low efficiency, high risk, and is easily restricted by external environmental factors. Unmanned air vehicle (UAV) inspections have emerged to resolve the security problems to a certain extent. The drones-based high-definition inspection pictures are sent back to the ground for manual processing, but this method is still inefficient, and the missed detection rate and false detection rate are relatively high. Current deep learning technique has yielded more target detection algorithms for transmission line inspections. The challenging issues for inspection picture are derived of the size of the bolt structure and its small proportion and its complicated background. Existing target detection algorithms are oriented to obtain feature maps by continuously up-sampling the pictures input to the network. However, the scale of the feature maps tends to be quite smaller in the continuous up-sampling process. The loss of visual detail information in the feature map can get positioning and classification effects better, which is incapable to the recognition and detection of bolt-relevant pins loss, and the detection effect is poor. In order to improve the detection effect of missing pins of bolt in transmission lines, we develop a method based on inter-level cross feature fusion. Method To detect multi-scale targets, the single shot multibox detector (SSD) based network is used to output six different scale feature maps. 1) The low-level large-scale feature maps are used to detect small targets, and the high-level small-scale feature maps are used to detect large targets.2) The anchor box mechanism is also introduced into the SSD to guarantee the overall detection in the feature map. Therefore, SSD algorithm is more suitable for detecting bolt-related missing pins in the inspection picture. First, the small target paste data augmentation is carried out on the bolt missing pins fault detection data set. After cutting out the parts corresponding to the missing-pins bolt category and randomly paste into larger-scale inspection pictures, the number of label boxes in the large-scale inspection pictures and the number of images in the data set are both increased to realize data augmentation. Next, the inter-level cross self-adaptive feature fusion module is introduced into SSD network. It can add the feature pyramid structure, improve its structure and increase the level of cross-connection between feature maps. The feature map of the Conv4_3 layer in SSD network is beneficial to the detection of missing-pins bolts. Feature maps of the Conv3_3 layer and the Conv5_3 layer are introduced in terms of the six-layer output feature maps-derived feature pyramid. The fusion of the Conv4_3 layer and the Conv8_2 layer is used to enhance the visual information and semantic information of the feature maps. At the same time, the adaptively spatial feature fusion (ASFF) mechanism is melted into the network to adaptively learn the spatial weights of feature map fusion at various scales, and the obtained weight fusion inspection feature map is used for the final detection. Finally, the K-means clustering method is employed to statistically analyze the size and aspect ratio of the labeled frame for the bolt structure, and the anchor box is adjusted in the original SSD network adequately. Result The verification experiments are performed for the effectiveness of the network on the PASCAL VOC(pattern analysis, statistical modeling and computational learning visual object classes) dataset. The improved network has reached a 2.3% growth in detection accuracy compared to the original SSD. In the bolt missing pins detection experiments, the training set and the test set are randomly divided according to the ratio of 7:3. Experimental results show that our detection accuracy is 87.93% for normal bolts, 89.15% for missing-pins bolts. The detection accuracy is increased by 2.71% and 3.99%, respectively. Conclusion Our method has greatly improved the accuracy of bolt-relevant pins loss detection. The detection accuracy of the original SSD network has been significantly improved. Our optimized detection is beneficial to further develop the recognition and detection of other parts in the transmission line.