HAFREE: A heatmap-based anchor-free detector for apple defects

Abstract

Precise inspection of subtle defects on apple surfaces is crucial in real-world agricultural scenarios. However, existing methods often rely on expensive equipment or struggle to detect visually ambiguous defects. To address this challenge, we introduce the Surface Subtle Defects Apple (SSDA) dataset, a custom dataset collected and annotated specifically for this study. Using the SSDA dataset, we propose HAFREE (Heatmap-based Anchor-Free detector), a novel end-to-end object detection architecture designed for localizing subtle defects on apple surfaces. Unlike traditional methods reliant on anchor boxes, our model leverages a heatmap-based detector that represents defects as keypoints using 2D Gaussian kernels. To address the scarcity of labeled data in real-world settings, we propose a patch training strategy that incorporates both full images and cropped patches during training, providing the network with both local and global contextual information. Furthermore, we introduce a Multi-scale Feature Fusion Block (MFFB) to enrich visual patterns and enhance overall performance. Our method achieves an mAP@.5 of 50.05% on the SSDA dataset, outperforming both two-stage and one-stage anchor-based detectors and state-of-the-art anchor-free approaches. HAFREE paves the way for a new paradigm in agricultural defect detection, offering an effective precise solution for real-world applications.