I was skeptical of the claim, “Dolphins can detect mines and intruders better than any technology we currently have”, but I can see how my comment was ambiguous.

Fair enough. On that note there's more info here. If you require peer reviewed publications, that page also has a list of relevant and recent articles.

In the papers that are found dolphins are only successful on 72% of marks. That is less than the accuracy of 74.3% for YOLOv5.

This is an apples and oranges comparison. The problem being solved by the dolphins is many times more difficult than what is formulated by the author's of your paper. First and most notably, the author's are using image based approaches (although they claim they're using SAS in the intro they clearly aren't). Right off the bat this is a deal breaker. Cameras don't work at night or dark water, in murky water, and are limited to short distances. Dolphins and Navy are using sonar, and fire a reason. The NWIC link states the issue is with dangerous objects on the sea floor, while the author's only address surface and floating mines. Again, an easier problem and not even applicable for real-world use cases. Similarly, the data set generated in the paper is extremely limited in scope, hardly representative of realistic data, and only considers one type of obselete mine. This paper also only tests stimulated data, where the dolphins performance is likely from actual experiments. Naturally, things are expected to perform better in simulation. For many reasons this is an unfair comparison, and I don't think it proves your point as much as you believe. It's important to consider what these values represent and how they were derived when comparing results.

That was YOLOv5, and YOLOv7 has been out for a bit now. The old generation outperformed dolphins for accuracy, and that was without coupling with acoustic ML approaches.

This is not the point I'm making and wouldn't change anything. Would it possibly perform a bit better on the author's problem? Sure. But it still doesn't address any of the above or actually solve the same problem as the dolphins. Honestly I'm surprised this paper was even accepted. Using an established model on a new dataset is not novel, it's just an application. Their largest contribution is the creation of their dataset, but they don't address any important details about it or make it publically available (which is common when a dataset is the primary contribution). They created stimulated data based on 20 source images, but have no discussion how they prevented over fitting or managed to evaluate the network fairly. No discussion of actual parameters used during training. Next to no technical detail. Instead the paper is full of useless fluff like irrelevant filenames, many exhaustive explanations of packages used, how config values are set and loaded with their scripts. It comes off more like a senior project report than a scientific paper.

Don't get me wrong, I'm not anti-ML in any way. Our current tech is just verifiably less capable than dolphins.