## 1 Introduction  
The girder bridge, as a modern urban transportation hub, is often an important target for both sides in combat to block the transportation of war materials, halt urban economic development, and paralyze the enemy, and it is important to to perform an assessment of the effects of the destruction of the bridge after the fight. It plays a very important role in the subsequent planning of fires [1]. The US Joint Chiefs of Staff defined the Battle Damage Assessment (BDA) as follows: The timely and accurate estimate of the damage resulting from the application of military force, either lethal or nonlethal, against a predetermined objective [2]. Since World War II, the US Army has continued to improve the means of damage assessment of strike targets, and since 2001, the US Army has held an annual Damage Battle Assessment conference, where new theories of and technology for damage assessment are discussed [3]. At present, the U.S. Army has formulated the \"Target Damage Assessment Quick Guide\" [4], \"Target Damage Assessment Reference Manual\", and other special orders and manuals to move damage assessment toward standardization, quantification, and rapid development [5]. Today, most countries have formed a comprehensive assessment information collection network of multiple sources such as reconnaissance satellites, manned and unmanned reconnaissance aircraft, airborne sensors, ballistic sensors, human intelligence, etc. [6]. The use of imagery for the target's damage assessment and the ability to remotely access the image to judge the target's damage situation (with the advantage of ensuring the safety of the personnel and a rapid response on the battlefield) have become the main method of battlefield damage assessment [7; 8].  
The current image-based assessment of the effects of damage to a girder bridge is mainly focused on the components of the bridge deck; it is not possible to assess the overall state of the bridge after being hit, and only using the bridge in the image to calculate the damage level of the changing features, such as geometric features and texture features, is very susceptible to external conditions and minor noise interference, resulting in low credibility and poor accuracy in the damage assessment results. In addition, the small number of samples regarding bridge destruction makes it difficult to apply machine learning-type methods.  
Based on the above problems, this paper proposes a method for assessing damaged girder bridges based on visible light images. The girder bridge is decomposed into two main sub-targets, the piers and the bridge deck, and an unmanned aerial vehicle (UAV) is used to capture the images, and the original images are pre-processed with image enhancement, image alignment, and change detection to identify and extract the pothole damage data. The damage characteristics of the remaining bearing capacity and the number of bridge through-lanes are proposed, the algorithm converting image features to damage features and the damage feature fusion algorithm are designed, and the damage level evaluation rules are established accordingly. These factors are combined with the pothole damage data, damage features, feature fusion results, and damage grade to calculate and complete the evaluation of the effect of the damage to the girder bridge.  
The innovations of this paper are as follows. (1) We establish a transformation relationship between post-damage bridge images and residual bridge load capacity. (2) We propose a visible image-based method to detect the number of passable lanes on post-damage bridges. (3) We perform experimental simulations via MATLAB and prove the effectiveness of the proposed method.  
This paper is organized as follows. Section 2 introduces the existing work on bridge damage assessment. Section 3 designs an algorithm for the extraction of damage indicators based on the characteristics of the image. The simulation system and experimental results are shown in Section 4. Section 5 concludes the paper.