## 5 User Study
### Testbed Environment  
We modeled and rendered the virtual bridge in the Unity game engine using SteamVR tracking (version 2 lighthouses) and presented the scene to participants via a wireless HTC Vive Pro headset with a 90 Hz display and 1440 \(\times\) 1600 pixels per eye. We used an Alienware Aurora R6 running Windows 10 with an Intel Core i7-7700 CPU and a NVIDIA GeForce GTX 1080 Ti graphics card to power the Vive Pro HWD. Concentrated areas of dots that were a different color from the surrounding bridge area delineated the target areas (Figure 1). Each target used the same dot pattern, but was rotated, or flipped randomly across the bridge surfaces so that the targets would not look the same, but would still have the same general features. Target saliency was manipulated by adjusting the transparency values of the target area. We used transparency values of 0.22 for the high saliency/easy condition, 0.17 for the medium saliency condition, and 0.12 for the low saliency/hard condition. Transparency values were obtained from a pilot study in which we adjusted transparency values until participants got an average of 80% correct for the easy condition, 60% correct for the medium condition, and 30% correct for the hard condition. No AR cues were present in this pilot study. Before each trial, targets were placed randomly on the bridge surface with a maximum of three targets per plane to prevent an excess of targets on any individual bridge section. Additionally, governing the maximum number of targets per plane limits user misses due  
Figure 1: Simulated bridge environment with all saliency levels and augmentation types represented. The top left target is the low saliency target (0.12) with the bounding box augmentation, the bottom left target is the medium saliency target (0.17) with the corner-bound augmentation, the top right target is the high saliency target (0.22) with the outline type augmentation, and the bottom right target is the non-augmented, high saliency target. The white line denotes the movement the simulated drone made during each trial. Note that this graphic is for visual representation only and that during experimental trials, only one saliency type and one augmentation type would appear for each trial. © 2021 IEEE. Reprinted, with permission, from [PERSON], [PERSON] and [PERSON], “Drone-based Augmented Reality Platform for Bridge Inspection: Effect of AR Cue Design on Visual Search Tasks,” _2020 IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Work._, pp. 201–204, 2020, doi:10.1109/VRW50115.2020.00040. ; [PERSON], [PERSON], and [PERSON], “Augmented Reality for Infrastructure Inspection with Semi-autonomous Aerial Systems: An Examination of User Performance, Workload, and System Trust,” _2020 IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Work._, pp. 743-744, 2020, doi:10.1109/VRW50115.2020.00220. to a physical inability to accurately select the targets even if they could visually identify them all. The cues used in the study were a corner-bound box, bounding box, and an outline of the dotted target area. We used a no-augmentation condition as a control. All augmentations were red (RGB: 255, 0, 0). See Figure 1 for a visual representation of the different target saliency levels and AR cue types. Cuing accuracy was a two-level, between-subjects variable in which we randomly assigned participants to either the high accuracy cuing aid or low accuracy aid condition. In the high accuracy group, 80% of the targets were hits (the aid correctly cued the target with the red hologram), 10% were false alarms (the aid cued a non-target area), and 10% were misses (the aid did not cue the target). In the low accuracy group, 40% were hits, 30% were false alarms, and 30% were misses.