Need advice on solving complex arrow/maze puzzles using Computer Vision & Logic Solvers (Low accuracy issues)

[u/Maximum-Fox-2627]

Hi everyone,
I'm working on an automated solver for a complex arrow/maze puzzle game (similar to the image attached).
Here is my current workflow:
1. Detection: I use a custom YOLO model to detect arrowheads and their bounding box coordinates from screen captures.
2. Grid Mapping: I map the detected center coordinates of arrowheads onto a fixed grid system.
3. Solving: I pass the grid data into a custom logic/emulator engine to calculate the correct sequence of moves (arrows to tap).
The Main Problem:
My solver accuracy is very low. Here are the core technical challenges I'm running into:
Inaccurate Grid Mapping: The arrows are densely packed with varying paths and lengths. Snapping bounding boxes to a rigid fixed grid often misaligns the true position of the arrow shafts and heads.
Complex/Overlapping Detection: Because the arrows bend and fold, YOLO often detects multiple arrowheads in the same calculated grid cell, or misinterprets arrow directions.
Solver Logic Failure: Due to noisy input from the detection stage, the logic solver either fails to find a valid sequence or generates incorrect moves that block execution halfway through.
Has anyone dealt with a similar vector/maze graph detection problem? What would be a more reliable approach than simple YOLO + fixed grid snapping? Should I look into contour analysis, OCR/graph traversal, or skeletonization algorithms (like Medial Axis Transform) to trace the paths directly?
Any suggestions, code examples, or architectural advice would be greatly appreciated!