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The use of augmented reality (AR) with semi-autonomous aerial systems in civil infrastructure inspection offers an extension of human capabilities by enhancing their ability to access hard-to-reach areas, decreasing the physical requirements needed to complete the task, and augmenting their visual field of view with useful information. Still unknown though is how helpful AR visual aids may be when they are imperfect and provide the user with erroneous data. A total of 28 participants flew as an autonomous drone around a simulated bridge in a virtual reality environment and participated in a target detection task. In this study, we analyze the effect of AR cue type across discrete levels of target saliency by measuring performance in a signal detection task. Results showed significant differences in false alarm rates in the different target salience conditions but no significant differences across AR cue types (none, bounding box, corner-bound box, and outline) in terms of hits and misses.