[HN Gopher] WALDO: Whereabouts Ascertainment for Low-Lying Detec...
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WALDO: Whereabouts Ascertainment for Low-Lying Detectable Objects
Author : jonbaer
Score : 55 points
Date : 2024-10-02 17:56 UTC (5 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| Jerrrrrrry wrote:
| This is a perfect module for an ideal proactive security system,
| thank you.
| throwup238 wrote:
| _> 'arm/mil' --> this class detects certain types of armored
| vehicles (very unreliable for now, don't use it yet)_
|
| Living near a bunch of the military bases, this is what I really
| need. My suburban defense system keeps mistaking USPS trucks for
| APCs.
|
| I haven't received any mail for months.
|
| Sidenote: what are the export restriction?
| stephanst wrote:
| that class never really worked and has been removed from the
| new version of WALDO FYI, it's not a military thing and
| shouldn't be used as such
| AtlasBarfed wrote:
| Ai is going to super charge off grid antigov nuts libertarians?
| PTOB wrote:
| Not just them. I predict it will extend to all classes of
| folks who use popular schemas for naming taken from self-
| isolating social forums.
| ulnarkressty wrote:
| What would be legitimate civilian uses for this technology apart
| from [0]? After the 10k drone swarm the other day and the pager
| attacks all I can think of is slaughterbots, which is genuinely
| freaking me out.
|
| [0] - https://xkcd.com/2128/
| throwup238 wrote:
| _> The basic model shared here, which is the only one published
| as FOSS at the moment, is capable of detecting these classes of
| items in overhead images ranging in altitude from about 30 feet
| to satellite imagery with a resolution of 50cm per pixel or
| better._
|
| It's not just for drones, it's for any overhead imaging.
|
| This can be used for all kinds of things like search and
| rescue, traffic monitoring, watching for wildfires, disaster
| response, monitoring parking lots as an economic indicator,
| etc.
| Nadya wrote:
| Depending from how high it can reliably work from, collaborate
| with UK CCTV surveillance so that you can better track
| individuals with fewer cameras as long as you can collate them
| with cameras that confirm their position at various points in
| time.
|
| Fly a handful of drones over the area of a fleeing suspect and
| be able to track their whereabouts and look for suspicious
| behaviors (eg. someone running and making constant turns in a
| city or doubling back often, cutting through alleys).
|
| Hell fly a few drones of the city to monitor foot traffic of
| the population and determine possible points of interest for
| new developments. Where are people walking to? How do they tend
| to get there? Can we optimize traffic for them - or more
| realistically - around them?
|
| Could be used for other forms of crowd analysis too such as how
| to best disperse a riot and separate a crowd.
|
| Sorry I guess I'm about as pessimistic as you are about it. Use
| in S&R like throwup238 suggested seems like a good non-
| militaristic fit for it.
|
| Oh and also this which was posted on HN not too long ago:
| https://dropofahat.zone/
| wongarsu wrote:
| Buy satellite images of walmart parking lots, run this model to
| count the cars. Repeat this every week, buy walmart stock when
| the number goes up and short walmart when the number goes down.
|
| Buy satellite images of container ports, count the number of
| containers, predict performance of economy based on containers
| and invest accordingly.
|
| Presidential candidate has an open-air rally and you want to
| figure out how many people are attending? Buy a satellite image
| scheduled for that exact hour and let WALDO count the people.
|
| Financing a number of large construction projects but don't
| trust the progress reports? Buy regularly scheduled satellite
| images and let WALDO count the number of trucks and
| construction vehicles.
|
| Want to invest in the construction business? Guess what, buy
| satellite images, count trucks and construction vehicles, make
| investment decisions based on that
| Arubis wrote:
| Giving credit where it's due, this project is almost worth doing
| just to be able to use that fabulously-well-fit initialism.
| lagniappe wrote:
| I'm pretty impressed. I miss the days of cool codenames and
| initialisms.
| stephanst wrote:
| this was >50% of the motivation
| swayvil wrote:
| So it gives us, for all the objects in view, a unique id,
| location, location history, various alerts.
|
| What else? Any thing-description?
|
| If an object leaves the view and re-enters, does it get the same
| id?
| syassami wrote:
| It's just yolo-esque classid, bbox coords, confidence. You'll
| have to implement some sort of tracking algorithm to get your
| other traits.
| adontz wrote:
| I wonder if these achievements are related to war in Ukraine. Do
| scientists suddenly receive more funding or something? Or it just
| happens?
|
| Is there a non public version with very reliable arm/mil? Is
| there a version which can reliably distinguish T-80 with and
| without Z?
| wongarsu wrote:
| A big part is that training image detection is incredibly easy
| today. YOLO is a great network with reasonably intuitive
| tooling. Anyone with a set of images can start labeling them,
| copy-paste a couple lines into a jupyter notebook and make a
| decent YOLO finetune.
|
| The difficulty is in the training data, both acquiring it and
| labeling it. Hence why the readme of WALDO alludes so much to
| their semi-synthetic data. That's also why this commercial
| project is happy to give out the models, but doesn't publish
| their data pipeline.
|
| If you have about 100 satellite images each of T-80s with and
| without Zs, and a couple other satellite images of other tanks
| and of landscapes without any tanks you can train a T-80
| detecting model in a couple hours. And then spend a couple days
| in a rabbit hole where you figure out that because in your
| training set only images with tanks had smoke clouds the model
| now thinks that smoke clouds are linked to tanks, and you end
| up making larger and larger data sets with tanks and non-tanks
| from all angles.
| Grosvenor wrote:
| Cool. But doesn't include the training data. So can't reproduce
| it. :-(
| stephanst wrote:
| Hey, thanks for posting. New release is coming tomorrow on HF
| BTW. AMA
| patches11 wrote:
| Cool project, any specific reason you went with YOLOv7?
|
| I know you aren't going to release the dataset but I'd be
| interesting in any info you are willing to share on
| augmentations you used and how you generated the synthetic
| imagery, and what sort of lift you got out of it.
| stephanst wrote:
| Some of the design choices of YOLOv7 make more sense to me in
| the choices of default augmentations and the structures of
| the very large versions of the networks. I find I can push it
| to marginally better recall. It's slower than Ultralytics' V8
| but if you want to do stuff like offline processing of
| satellite imagery for instance or get 1fps on occupancy of a
| parking lot that kind of performance really doesn't matter.
| throwawaymaths wrote:
| I worked for a place where we needed to know with precision where
| in space a large object was relative to a large area we had full
| control over. I wonder if this could be used in reverse by say
| dropping QR codes on the ground, using the algorithm to track
| relative positions and doing the reverse operation from there
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