## 1 Introduction  
Effective bridge inspection and assessment techniques are a growing concern and challenge to stakeholders, investors, practitioners, highway users, and concerned government institutions. The deteriorating condition of the over 619,588 bridges across the United States, of which 7.5% are considered structurally deficient, has motivated the US Government to provide and deploy a USD 40 billion bridge fund program to states for bridge repairs through the year 2026. However, this fund still cannot cater to a backlog of nationwide bridge repair funding needs of about USD 125 billion [1]. Ratings of bridges are determined by human and visual inspection of the various parts of the bridges. However, traditional methods of inspections, such as physical and visual inspections, are hindered by several limitations, such as variability and inconsistencies in designated condition ratings by different inspectors. These inconsistencies are usually affected by factors such as fear of traffic, near visual acuity, color vision, formal training on bridge inspection, dependency on skill or experience, accessibility, and complexity of structure [2; 3; 4; 5]. In addition, an inspection of the bridge structure is usually by contact, which involves a significant setback, such as lane closure, traffic disruption, hard-to-reach areas (sub-surface damages), personnel safety, accident-related issues, cost, and time consumption [3]. Unmanned and autonomous inspections with artificial intelligence (AI) are potentially viable techniques aiding conventional and traditional bridge inspection methods at a fast-growing pace [6]. In recent decades, automation has gained ground in bridge inspection to help alleviate and offset the problems and challenges faced using traditional methods. Several non-destructive techniques have been used to investigate sub-surface defects in bridge decks, such as sound techniques (hammer sounding, chain dragging, and impact echo). Other methods are electric resistivity (ER), ultrasonic surface wave (USW), ground penetration radar (GPR), and visual sensor imaging [7; 8; 9; 10]. However, these methods are not effective in the detection of sub-surface delamination. Unmanned aerial systems (UAS) mounted with sensors such as infrared thermography (IRT) in recent years have proven to be effective in collecting NDE data and assessing sub-surface defects of bridge structures, fatigue crack detection in steel bridges with fracture critical members (FCM), and corrosion detection in ancillary structures [11; 12]. The UAS-IRT method assesses bridges in a timely, effective, and cost-efficient manner, revealing sub-surface defects, minimizing traffic closures, and ensuring the safety of inspectors, providing access to difficult areas inaccessible by traditional methods [13; 14; 15; 16; 17]. Autonomous inspections performed by platforms such as robots or UAS are usually image-based. The dataset collected during the inspection is either processed visually by an inspector or autonomously by a computer algorithm/model. Previous studies have mainly demonstrated image-based inspections using computer vision and deep learning techniques in crack detection, spalls, and corrosion in concrete bridges, bearing displacement, and bolt loosening, but very few on delamination [12; 18; 19; 20; 21; 22; 23]. Past studies have detected sub-surface delamination using image processing methods and deep convolutional neural networks (DCNN) models on laboratory-prepared specimens. Most of these studies are usually investigated mainly on laboratory specimens that do not depict the on-site and environmental constraints for IRT data collection [13; 24]. The few studies on delamination detection of bridge decks are image-based only, where inspectors visually process and classify the images based on pixel intensity or visual characterization. The image-based method is usually based on temperature difference and pixel-contrasting thresholding techniques.