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

arXiv: https://arxiv.org/abs/1904.02805 · HTML: https://mlsanity.com/paper/1904.02805/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 5 of 5)

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- [Comparing Visual Reasoning in Humans and AI](https://mlsanity.com/paper/2104.14102/) (2021)
- [The Effects of Image Distribution and Task on Adversarial Robustness](https://mlsanity.com/paper/2102.10534/) (2021)
- [Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals](https://mlsanity.com/paper/2311.14720/) (2023)
- [Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models](https://mlsanity.com/paper/2607.09654/) (2026)

## Abstract (arXiv)

With the advent of modern expert systems driven by deep learning that supplement human experts (e.g. radiologists, dermatologists, surveillance scanners), we analyze how and when do such expert systems enhance human performance in a fine-grained small target visual search task. We set up a 2 session factorial experimental design in which humans visually search for a target with and without a Deep Learning (DL) expert system. We evaluate human changes of target detection performance and eye-movements in the presence of the DL system. We find that performance improvements with the DL system (computed via a Faster R-CNN with a VGG16) interacts with observer's perceptual abilities (e.g., sensitivity). The main results include: 1) The DL system reduces the False Alarm rate per Image on average across observer groups of both high/low sensitivity; 2) Only human observers with high sensitivity perform better than the DL system, while the low sensitivity group does not surpass individual DL system performance, even when aided with the DL system itself; 3) Increases in number of trials and decrease in viewing time were mainly driven by the DL system only for the low sensitivity group. 4) The DL system aids the human observer to fixate at a target by the 3rd fixation. These results provide insights of the benefits and limitations of deep learning systems that are collaborative or competitive with humans.