FBMS-YOLO improves localization of degraded bamboo slip characters in benchmark testing

A task-specific detector called FBMS-YOLO improved character-level localization on the DeepJiandu bamboo slip benchmark, raising mean average precision at an intersection-over-union threshold of 0.5 from 92.8% for the YOLOv11-n baseline to 94.4%. In the same evaluation, mean average precision averaged across IoU thresholds from 0.50 to 0.95 increased from 55.0% to 56.8%. The computational study evaluated infrared images of excavated bamboo slips, where characters can be narrow, fragmented, densely arranged and obscured by fading, cracks, stains and wood-grain textures.

The researchers developed Fine-Grained Bidirectional Multi-Scale YOLO, or FBMS-YOLO, as a task-driven reconfiguration of YOLOv11 rather than a wholly new detector. Its three main components address different parts of the localization problem. The Attention-enhanced Refinement and Context Network, or ARCNet, models slender and fragmented strokes; the Fine-grained Bidirectional Feature Pyramid Network, or FB-FPN, preserves high-resolution details during cross-scale fusion; and the Dynamic Focusing and Boundary-Penalty Intersection over Union loss, or DFB-IoU, guides bounding-box regression when character edges are ambiguous.

ARCNet replaces the baseline C3k2 feature-extraction blocks with Cross Stage Partial Hierarchical Progressive Multi-scale Feature Aggregation modules, abbreviated CSP-HPMFA. Each module retains a lightweight branch for complementary features and processes another through partial channels with progressive 3×3, 5×5 and 7×7 receptive fields. The authors designed this arrangement to represent short fragments and long slender structures while avoiding the redundant full-channel computation associated with applying large kernels to every feature. The existing AIFI attention component is also used at the deepest semantic stage to model long-range context, including the vertical arrangement of characters and extended material patterns.

FB-FPN replaces direct upsampling-and-concatenation fusion with a Bidirectional Adaptive Fusion Module, or BAFM. Sigmoid gates estimate the reliability of high- and low-level features, allowing semantic information to guide shallow boundary details while low-level information feeds back toward the deeper representation. The framework also extends detection from the original P3–P5 levels to P2–P5, adding a higher-resolution prediction level for weak traces, small fragments and fine character contours. This higher-resolution branch is integrated into bidirectional fusion rather than appended as an independent prediction layer.

For localization supervision, DFB-IoU combines a normalized penalty for differences between the four predicted and ground-truth box boundaries with the dynamic focusing mechanism used in WIoUv2. The dynamic term adjusts each sample’s regression weight according to its localization difficulty relative to an exponential moving average of IoU loss. Unlike CIoU, the proposed loss does not explicitly impose an aspect-ratio consistency term. The authors state that this boundary-based approach is intended for characters whose faded, incomplete or visually entangled edges can make geometric aspect-ratio penalties unstable.

The main DeepJiandu dataset contains 7,416 infrared bamboo slip images with 99,852 annotated character instances across 2,242 categories, divided into 5,922 training, 751 validation and 743 testing images. For this localization study, all annotations were merged into one foreground class. Models were trained for 100 epochs at 640×640 input resolution on a single NVIDIA RTX 5090. The main comparisons and ablations used a fixed seed, while five independent runs with seeds 0 through 4 were used for statistical testing. Welch’s two-sample t-tests found mean mAP50 of 94.32% for FBMS-YOLO versus 92.88% for YOLOv11-n, with p=5.93×10⁻⁶, and mean mAP50:95 of 56.74% versus 54.90%, with p=3.58×10⁻⁷.

FBMS-YOLO used 4.3 million parameters and 17.7 GFLOPs, compared with 41.3 million parameters and 197 GFLOPs for Faster R-CNN. It achieved 94.4% mAP50, 1.3 percentage points above Faster R-CNN’s reported score, while its 56.8% mAP50:95 was 0.1 point below Faster R-CNN’s 56.9%. The accuracy gains came with additional model and computational cost relative to YOLOv11-n, so the authors characterized FBMS-YOLO as an accuracy-oriented detector rather than a lightweight replacement. Its reported inference speed was 71 frames per second, down from 109 frames per second for the baseline in the ablation setup.

Progressive ablation results associated the largest early gains with adaptive fusion and high-resolution prediction. Adding BAFM increased mAP50 from 92.8% to 93.3% and mAP50:95 from 55.0% to 55.8%; adding the P2 level produced 93.7% and 56.0%, respectively. CSP-HPMFA raised those measures to 94.1% and 56.3%, while AIFI further increased mAP50:95 to 56.6%. DFB-IoU then brought the complete model to 94.4% and 56.8% without changing parameter or GFLOP counts. In a separate cross-dataset experiment on 9,306 annotated oracle bone inscription rubbing images, the architecture increased F1 from 83% to 84%, mAP50 from 87.7% to 89.8% and mAP50:95 from 52.5% to 54.1%. The dataset had 8,895 training and 411 validation images but no separate test set, so those results were measured on the validation set.

The study identifies several bounded limitations. Visually connected adjacent characters may be interpreted as one region, and stroke-like cracks or fractured slip edges can still produce false positives. When an annotated boundary is itself ambiguous, the predicted box may cover only the most visible fragments. The oracle bone experiment demonstrated transfer to a different historical character material, but it was limited to bounding-box localization on one dataset and did not test broader domains or fine-grained character recognition. The authors propose future work on instance-level contour modeling, degradation-aware negative learning, structure-preserving losses, anisotropic or deformable convolution, cross-domain learning and joint detection and recognition.

Subject of Research: Benchmark evaluation of an object detector for locating degraded bamboo slip characters

Article Title: A fine-grained bidirectional multi-scale network for extreme-aspect-ratio bamboo slip character detection

Article References: Gao, F., Bao, C., Kong, M., & Chen, B. (2026). A fine-grained bidirectional multi-scale network for extreme-aspect-ratio bamboo slip character detection. Discover Artificial Intelligence, 6(1), Article 1275. https://doi.org/10.1007/s44163-026-01976-9

Image Credits: AI Generated

DOI: 10.1007/s44163-026-01976-9

Keywords: bamboo slip characters, object detection, FBMS-YOLO, YOLOv11, cultural heritage digitization, computer vision, feature fusion, boundary localization, DeepJiandu, fine-grained, bidirectional, multi-scale