🤖 AI Summary
This study addresses the challenge of accurately counting eucalyptus logs in complex scenarios involving occlusion, overlap, and cluttered backgrounds. To this end, it introduces conditional generative adversarial networks (cGANs) for the first time to this task, complemented by tailored image pre- and post-processing strategies that effectively mitigate noise and adhesion artifacts. Log counts are derived through connected component analysis, enabling efficient and robust enumeration. The authors construct and publicly release a large-scale dataset comprising 466 images. Evaluated on an NVIDIA T4 GPU, the proposed method achieves real-time performance with an average processing time of 0.713 seconds per image. Experimental results demonstrate a pixel-level accuracy of 96.4%, a log counting accuracy of 92.3%, F1 scores ranging from 0.879 to 0.933, and Intersection over Union (IoU) values between 0.784 and 0.875.
📝 Abstract
This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional Generative Adversarial Networks (cGANs) for eucalyptus log segmentation in images, incorporating specialized image processing techniques to handle noise and intersections, coupled with the Connected Components Algorithm for efficient counting. To support this research, we created and made publicly available a comprehensive database of 466 images containing approximately 13,048 eucalyptus logs, which served for both training and validation purposes. Our method demonstrated robust performance, achieving an average Accuracy_pixel of 96.4% and Accuracy_logs of 92.3%, with additional measures such as F1 scores ranging from 0.879 to 0.933 and IoU values between 0.784 and 0.875, further validating its effectiveness. The implementation proves to be efficient with an average processing time of 0.713s per image on an NVIDIA T4 GPU, making it suitable for realtime applications. The practical implications of this method are significant for operational forestry, enabling more accurate inventory management, reducing human errors in manual counting, and optimizing resource allocation. Furthermore, the segmentation capabilities of the model provide a foundation for advanced applications such as eucalyptus stack volume estimation, contributing to a more comprehensive and refined analysis of forestry operations. The methodology's success in handling complex scenarios, including intersecting logs and varying environmental conditions, positions it as a valuable tool for practical applications across related industrial sectors.