# Food Waste Detection in Canteen Plates Using YOLOv11
## 4 Experiments and Results
### Exploration and Results  
In the previous sections, we presented the context, the dataset, the calculation formula, the YOLO benchmark focusing on version 11 as well as the platform architecture. Based on this structure, we developed a web platform that allows for the exploration of this work in detecting food waste on plates. We tested the platform using 50 images of plates after meals and evaluated the model. Some of these images were previously shown from the dataset and are illustrated in the following figure. Figure 11 presents visual examples of the detection performed by the platform on different food plates. In images Figure 10(a),b,d, multiple objects on the plate are detected, with clustering applied to each identified item, such as cutlery, food, and waste. These images illustrate the platform's ability to segment and correctly classify various elements present on the plates after the meal. In contrast, image Figure 10(c) shows only the result of clustering applied to the food, without individual object detection, serving as a comparative baseline of the platform's operation under different analysis modes. As an illustration, for the four images presented in Figure 11, Table 5 presents the results regarding food waste percentage and the wasted food. In Figure 10(a), the platform correctly identified the cutlery (knife and forks) and a cup; however, it incorrectly detected a plate with traces of soup, which likely led to an inaccurate waste percentage calculation due to the misidentification. In Figure 10(b), a plate with food remnants and utensils is observed, where the platform successfully detected, in addition to the cutlery and cup, the presence of garbage beneath the plate, demonstrating the model's ability to correctly distinguish waste. In Figure 10(c), the platform confidently identified the presence of french fries and rice, as well as a plate and a partially visible knife, highlighting its accuracy in detecting uneaten food. Finally, in Figure 10(d), the detection covered a wide range of elements, including rice, garbage, potatoes, cutlery, and additional items such as a cup and a spoon, reinforcing the platform's robustness even in more complex scenarios with multiple items on the plate. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware** & **Specification** \\ \hline OS & Ubuntu 24.04 \\ CPU & Intel Xeon E5-2650 (10 cores) \\ CPU Sockets & 2 \\ RAM & 24 GB DDR3 \\ GPU & — \\ DISK & 128 GB HDD SAS \\ VIRTUALIZATION & Virtual Machine (KVM) \\ MANUFACTURER & Hewlett Packard Enterprise, Palo Alto, CA, USA \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Server specifications. The platform output illustrated in Figure 12 presents, on one side, the detected image, the clustered image, and the waste calculation result, respectively. Additionally, the interface includes a section that describes how the calculation was performed. All images were processed by the proposed platform, as exemplified in Figure 12, where object detection and clustering outputs are generated, followed by the calculation of the corresponding waste percentage. The resulting data from this process are presented in the table below, aiming to demonstrate the platform's effectiveness across different real-world scenarios. Figure 11: Exploration results for some image detections. \begin{table}
\begin{tabular}{c c c} \hline
**Image** & **Waste Percentage** & **Wasted Food** \\ \hline Figure 11a & 0.00 & – \\ Figure 11b & 0.00 & – \\ Figure 11c & 100.00 & Rice, French Fries, Breaded \\ Figure 11d & 42.84 & Rice, Baked Potatoes \\ \hline \end{tabular}
\end{table} 
Table 5: Waste percentage detected on the sample images from Figure 11.  
Additionally, we tested the platform with food plate images from the following datasets: TossIt Plates Computer Vision Project [45], Finding Defects Computer Vision Project [46], and FoodWasteDetectionV2 Computer Vision Project [47]. Some examples of these images are illustrated in the following figure.