PROJECTS

[tulip lodge]

Is there anyone interested in doing real time deep learning projects.....let learn together

[deep kite]

i am interested

[tulip flare]

Me

[fluid fable]

I am interested

[hot harbor]

Iam interested

[still grail]

Elaborate

[tulip lodge]

a unified safety model for industrial sites that covers
Context-aware PPE detection (using object detection models like Faster R-CNN, checking if workers wear the right PPE for their task)

Fire and smoke detection (via a classifier/detector trained on open datasets)

Fall detection (using pose estimation models like OpenPose or MediaPipe to spot unsafe postures or sudden falls)

combining all three into one system for real-time safety monitoring.

[still grail]

@tulip lodge Abstract
Workplace accidents due to personal protective equipment (PPE) non-compliance raise serious safety concerns and lead to legal liabilities, financial penalties, and reputational damage. While object detection models have shown the capability to address this issue by identifying safety items, most existing models, such as YOLO, Faster R-CNN, and SSD, are limited in verifying the fine-grained attributes of PPE across diverse workplace scenarios. Vision language models (VLMs) are gaining traction for detection tasks by leveraging the synergy between visual and textual information, offering a promising solution to traditional object detection limitations in PPE recognition. Nonetheless, VLMs face challenges in consistently verifying PPE attributes due to the complexity and variability of workplace environments, requiring them to interpret context-specific language and visual cues simultaneously. We introduce Clip2Safety, an interpretable detection framework for diverse workplace safety compliance, which comprises four main modules: scene recognition, the visual prompt, safety items detection, and fine-grained verification. The scene recognition identifies the current scenario to determine the necessary safety gear. The visual prompt formulates the specific visual prompts needed for the detection process. The safety items detection identifies whether the required safety gear is being worn according to the specified scenario. Lastly, the fine-grained verification assesses whether the worn safety equipment meets the fine-grained attribute requirements. We conduct real-world case studies across six different scenarios. The results show that Clip2Safety not only demonstrates an accuracy improvement over state-of-the-art question-answering based VLMs but also achieves inference times two hundred times faster.

Like this

[tulip lodge]

this sounds really innovative! The combination of vision-language models with fine-grained PPE verification is a strong approach for workplace safety. Thanks for sharing this with me!

[still grail]

No problem 🫡

[tulip lodge]

Do you have prior experience with VLMs or similar safety compliance systems? If you’re open to it, I’d love to learn from your insights and possibly get some guidance on how to approach this area

[still grail]

https://arxiv.org/html/2408.07146v1

Is where that info came from..

No my good sir...
Not the experience you're looking for..

I'm working on my own project right now.

I have created a Recursive a.i..
Able to tap and txt on any website it visits and learn from it.

[tulip lodge]

Ah got it 😅 no worries!

[still grail]

Well good luck with your project

[hardy compass]

intersted

[wind raft]

yes

[floral sail]

I am interested in it. Dm me if you require any details or have anything to converse on