Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. coli (0, 10⁵, 10⁷, and 10⁸ colony forming units (CFU)/mL) and two egg solutions (white and yolk) were applied to stainless steel and white rubber to simulate realistic contamination with organic interference. For each concentration level, 256 droplets were inoculated in 16 groups, and fluorescence videos were captured. Droplet regions were extracted from the video frames, subdivided into quadrants, and augmented to generate a robust dataset, ensuring 3–4 droplets per sample. Wavelet-based denoising further improved image quality, with Haar wavelets producing the highest Peak Signal-to-Noise Ratio (PSNR) values, up to 51.0 dB on white rubber and 48.2 dB on stainless steel. Using this dataset, multiple deep learning (DL) models, including ConvNeXtBase, EfficientNetV2L, and five YOLO11-cls variants, were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize model attention to bacterial fluorescence regions. Across four dataset groupings, YOLO11-cls models achieved consistently high performance, with peak test accuracies of 100% on white rubber and 99.60% on stainless steel, even in the presence of egg substances. YOLO11s-cls provided the best balance of accuracy (up to 98.88%) and inference speed (4–5 ms) whilst having a compact size (11 MB), outperforming larger models such as EfficientNetV2L. Classical machine learning models lagged significantly behind, with Random Forest reaching 89.65% accuracy and SVM only 67.62%. Overall, the results highlight the potential of combining UV-C fluorescence imaging with deep learning for rapid and reliable detection of E. coli on stainless steel and rubber conveyor belt surfaces. Additionally, this approach could support the design of effective interventions to remove E. coli from food processing environments.

Rapid, non-destructive assessment of Escherichia coli contamination levels on food-contact surfaces is important for food safety monitoring and sanitation verification. This study developed a deep-learning framework integrating UV-C fluorescence imaging and cascaded denoising to classify predefined E. coli concentration levels on four food-contact surfaces. A SafetySpect CSI-D+ handheld system acquired fluorescence images at four concentration levels: Control (0), 10⁶, 10⁷, and 10⁸ CFU/mL, on stainless steel, white rubber, white plastic, and black plastic. Five image-denoising operators (wavelet thresholding, NLM, unsharp masking, Wiener filtering, and RL-TV) were evaluated in sequential and parallel configurations. Eight deep-learning classifiers, including five YOLO11-cls variants, EfficientNetB7, ConvNeXtBase, and ViT, were benchmarked using consistent evaluation protocols. Classification performance varied with surface type and denoising strategy. The sequential Wavelet → Wiener → RL-TV cascade achieved the highest observed mean test accuracy (94.18%), with surface-specific accuracies of 97.57%, 97.57%, 94.44%, and 87.15% for stainless steel, white rubber, black plastic, and white plastic, respectively. On the most challenging surface (white plastic), the cascade improved accuracy by 11.46 percentage points over the best single operator. YOLO11-based models generally outperformed ViT, although the highest-performing architecture varied by surface. Most misclassifications occurred primarily between 10⁶ and 10⁷ CFU/mL, while the 10⁸ CFU/mL class was consistently classified accurately. Overall, the ordered cascaded denoising can improve robust E. coli concentration classification across diverse food-contact surfaces, providing a promising foundation for rapid, non-destructive food safety inspection. The results also highlight the importance of surface-specific preprocessing and deep learning architectures for robust performance across diverse materials.