###### Abstract  
Online autonomous perception of pantograph catenary system status is of great significance for railway autonomous operation and maintenance (RIOM). Image sensors combined with an image processing algorithm can realize the automatic acquisition of the pantograph catenary condition; however, it is difficult to meet the demand of long-term stable condition acquisition, which restricts the implementation of online contact state feedback and the realization of railway automation. This paper proposes an online intelligent perception of the pantograph and catenary system (PCS) status based on parameter adaptation to realize fast and stable state analysis when the train is in long-term operation outdoors. First, according to the feature of the contact point, we used histogram of gradient (HoG) features and one-dimensional signal combined with a KCF tracker as the baseline method. Then, a result discriminator located by L1 and hash similarity constraints was used to construct a closed-loop parameter adaptive localization framework, which retrieves and updates parameters when tracking failure occurs. After that, a pruned RefineDet method was used to detect pantograph horns and sparks, which, together with the contact points localization method, ensure the long-term stability of feature localization in PCS images. Then, based on the stereo cameras model, the three-dimensional trajectory of the whole pantograph body can be reconstructed by the image features, and we obtained pantograph catenary contact parameters including the pantograph slide posture, contact line offset, arc detection, separation detection, etc. Our method has been tested on more than 16,000 collected image pairs and the results show that the proposed method has a better positioning effect than the state-of-art method, and realizes the online acquisition of pantograph catenary contact state, representing a significant contribution to RIOM. intelligent systems; pantograph catenary system; contact state +
Footnote †: journal: Applied Sciences  
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Footnote 1: _Applied Sciences_  
contact and non-contact automatic measurement [5; 6]. The contact method effectively eliminates signal interference and reduces technical difficulties. It also has some applications in the measurement of center line offset but has inherent disadvantages related to environmental adaptability and convenience of placement. With the rise of computer vision technology, the non-contact method has been widely used in practical fault detection. Led by traditional railway companies such as Mermec, SelectraVision, Camlin, etc., the non-contact method has been developed into products in the fields of pantograph slide wear detection and contact wire morphology detection, which has brought technical innovations to the field of pantograph catenary fault detection. However, an actual PCS fault is mainly caused by the rapid movement of the train, which is hard for offline detection methods to solve. Offline methods cannot provide timely feedback before or after an actual PCS fault occurs, only playing a role in prevention or checking after a fault, so there is a strong demand for online detection. With regard to online detection, current research is mainly based on contact detect and non-contact detect. The contact detect generally analyzes the vibration signals, optical fiber signals, and detect pantograph conditions. For example, [PERSON] et al. [7] focus on the influence of car body bumping on pantograph and catenary system. [PERSON] et al. [8] give the attribution analysis of vibration and carbon slide wear, and [PERSON] et al. [9] give the pantograph catenary monitoring system combined with optical fiber sensors and image sensors, which can identify common pantograph catenary faults. The non-contact detect is realized by observing and analyzing the image of the pantograph and catenary through the image sensor on the roof of the train, which can give a more intuitive analysis of the interaction of the pantograph and catenary, but it requires a higher reliability of the algorithm.