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With the advent of modern expert systems driven by deep learning thatsupplement human experts (e.g. radiologists, dermatologists, surveillancescanners), we analyze how and when do such expert systems enhance humanperformance in a fine-grained small target visual search task. We set up a 2session factorial experimental design in which humans visually search for atarget with and without a Deep Learning (DL) expert system. We evaluate humanchanges of target detection performance and eye-movements in the presence ofthe DL system. We find that performance improvements with the DL system(computed via a Faster R-CNN with a VGG16) interacts with observer's perceptualabilities (e.g., sensitivity). The main results include: 1) The DL systemreduces the False Alarm rate per Image on average across observer groups ofboth high/low sensitivity; 2) Only human observers with high sensitivityperform better than the DL system, while the low sensitivity group does notsurpass individual DL system performance, even when aided with the DL systemitself; 3) Increases in number of trials and decrease in viewing time weremainly driven by the DL system only for the low sensitivity group. 4) The DLsystem aids the human observer to fixate at a target by the 3rd fixation. Theseresults provide insights of the benefits and limitations of deep learningsystems that are collaborative or competitive with humans.

Assessment of Faster R-CNN in Man-Machine collaborative search