SnapAPI
  • Models
  • Docs
  • Pricing
  • Blog
Log InSign Up
Log In
SnapAPI

One unified API to access world-class AI models.

Contact: support@silverai.com
Products
  • Models
  • Docs
  • Blog
Resources
  • API Updates
  • Terms
  • Privacy
Image Processing
  • Remove Background
  • Virtual Tryon
  • Enhance & Upscale
  • Object Detection
Image Generation
  • FLUX Kontext Dev
  • Z-Image Turbo
  • Qwen Image Edit
  • Fairy AI

©2026 SnapAPI.AI. All rights reserved.

All services are online
  1. Home
  2. Models
  3. Wire Detection
SilverAISilverAI

Wire Detection

Detect wires, cables and lines and return a mask for the Remove Wires API.

image

Overview

Wire Detection is an image analysis model that locates wires, cables, and thin line structures in a photo and returns a precise mask covering exactly those regions. It is built for the first stage of power-line and cable-removal pipelines, where you need to know where the wires are before deciding what to remove. Instead of guessing, the model produces a pixel-accurate RGBA mask that isolates even faint, hair-thin lines stretched across the sky or against busy backgrounds.

The detection output is designed to flow directly into the Remove Wires API. The returned mask can be passed as the input_mask parameter to that endpoint, either as-is or after manual touch-ups in your own editor. This separation of detection and removal gives you full control: you can review the mask, refine it, and only then trigger the actual inpainting step. The result is a reliable, auditable workflow for cleaning up overhead lines, telephone cables, and other distracting linear elements.

Key Capabilities

  • Pixel-precise thin-line masks: Captures hair-thin wires and long, low-contrast cables that conventional segmentation models miss.

  • Long-line awareness: Tracks continuous lines spanning the full width or height of an image without breaking the mask.

  • RGBA mask output: Returns a base64 RGBA PNG mask that can be previewed, edited, or composited before use.

  • Pairs with Remove Wires: The mask plugs straight into the Remove Wires API as input_mask for a clean detect-then-remove pipeline.

  • Editable by design: Masks are meant to be reviewed and manually refined, giving you control over exactly what gets removed.

  • Background-robust detection: Distinguishes wires from poles, branches, and architectural edges across varied scenes.

Supported Tasks

Detecting overhead power lines and electrical cablesLocating telephone and utility wires against sky or cluttered backgroundsGenerating masks for cable-removal and inpainting pipelinesProducing editable masks for manual refinement before removalPre-processing images for the Remove Wires API

Use Cases

Clean Skylines for Real Estate

Real estate and architectural photographers can detect overhead power lines crossing a property shot, generate a mask, and feed it into the Remove Wires API to deliver clean, distraction-free skylines that make listings look more polished.

Clean Skylines for Real Estate

Landscape and Travel Photography

Landscape and travel photographers often capture telephone and power cables that cut across an otherwise pristine view. Wire Detection isolates those lines precisely so they can be removed without disturbing the surrounding scenery.

Landscape and Travel Photography

Automated Image Cleanup Pipelines

Platforms processing large batches of images can use Wire Detection as a programmatic first step, automatically generating masks at scale and routing them to the Remove Wires API for hands-off, end-to-end cable cleanup.

Automated Image Cleanup Pipelines
Specifications
model iddetect-wires
vendorSilverAI
typeimage
inputRGB Image
outputRGBA Mask
endpoint/v1/images/detect-wires
statusStable
version1.0
Usage Pricing
Pay only for what you use
~$0.0005 / image
  • credits: 1 credit / image
  • credit rate: from $0.0005 per credit (top up via dashboard)
  • free tier: Available with API key signup
  • volume discounts: Available for high-volume usage

Save up to 70% vs direct pricing

Aggregated volume discounts.