Maya Pottery Red: Hue as a Perceptual Prior for Object Detection in UAV-Based Areal Survey.
Saved in:
| Title: | Maya Pottery Red: Hue as a Perceptual Prior for Object Detection in UAV-Based Areal Survey. |
|---|---|
| Authors: | Britton, Benjamin1 (AUTHOR) brittobj@mail.uc.edu, McLellan, Alec2 (AUTHOR), Dunning, Nicholas1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1836. 29p. |
| Subjects: | Aerial surveys, Object recognition (Computer vision), Deep learning, Loss functions (Statistics) |
| Geographic Terms: | Belize |
| Abstract: | Highlights: What are the main findings? Developed a Hue-Weighted Loss Function and Two-Phase Workflow for small-object detection. HSV-based filtering reduced candidates by 99.1% while retaining 97.8% of targets (F1: 0.731). What are the implications of the main findings? Chromatic priors can also assist search-and-rescue, environmental, and traffic detection. Low-altitude UAV chromatic detection scales survey records while reducing manual effort. The detection of small archaeological artifacts in high-resolution aerial imagery is challenged by minimal target size and local spectral and geometric similarity to background soils. This study identifies a failure mode in end-to-end deep learning where radiometrically dominant chromatic signals destabilize gradient-based optimization, leading to rapid training collapse. Using UAV imagery of Maya archaeological sites in Belize, we examine fingernail-sized ceramic sherds characterized by a consistent reddish hue. A Hue-Weighted Loss Function (HWLF) is introduced as a diagnostic instrument. Under severe class imbalance, chromatic gradients suppress geometric feature learning, collapsing detection within 300 iterations. Motivated by this discovery, we propose a staged detection architecture that decouples geometric candidate generation from chromatic validation. Candidates are detected via a transformer-based object detector and validated using hue constraints derived from unmodified 16-bit HSV representations. This approach reduced the Phase I candidate pool (177,148 geometric detections) to 1647 prioritized detections—a 99.1% reduction—while retaining 97.8% of annotated targets (F1 = 0.731). Chromatic priors may be more effective as decoupled post-inference discriminants than as embedded end-to-end optimization signals under severe class imbalance, where their gradient influence risks suppressing geometric feature learning entirely. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? Developed a Hue-Weighted Loss Function and Two-Phase Workflow for small-object detection. HSV-based filtering reduced candidates by 99.1% while retaining 97.8% of targets (F1: 0.731). What are the implications of the main findings? Chromatic priors can also assist search-and-rescue, environmental, and traffic detection. Low-altitude UAV chromatic detection scales survey records while reducing manual effort. The detection of small archaeological artifacts in high-resolution aerial imagery is challenged by minimal target size and local spectral and geometric similarity to background soils. This study identifies a failure mode in end-to-end deep learning where radiometrically dominant chromatic signals destabilize gradient-based optimization, leading to rapid training collapse. Using UAV imagery of Maya archaeological sites in Belize, we examine fingernail-sized ceramic sherds characterized by a consistent reddish hue. A Hue-Weighted Loss Function (HWLF) is introduced as a diagnostic instrument. Under severe class imbalance, chromatic gradients suppress geometric feature learning, collapsing detection within 300 iterations. Motivated by this discovery, we propose a staged detection architecture that decouples geometric candidate generation from chromatic validation. Candidates are detected via a transformer-based object detector and validated using hue constraints derived from unmodified 16-bit HSV representations. This approach reduced the Phase I candidate pool (177,148 geometric detections) to 1647 prioritized detections—a 99.1% reduction—while retaining 97.8% of annotated targets (F1 = 0.731). Chromatic priors may be more effective as decoupled post-inference discriminants than as embedded end-to-end optimization signals under severe class imbalance, where their gradient influence risks suppressing geometric feature learning entirely. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111836 |
