# Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
## 4 Results
### Results of Training Regression Based Networks  
The results shown in Table 3 were obtained by training the networks in [59] for a maximum number of batches of 4000 and passing each test dataset in Table 1 through the resulting networks to get the average precision (AP) values for IoU of 0.5 and 0.75. By looking at Table 3 and comparing all \(AP_{50}\), one can infer that the best dataset to use for training was the RGB dataset, and the best network was Tiny-YOLOv4, which yielded the best results, both with an IoU of 0.50 and an IoU of 0.75.  
The results observed by passing the test images through each network, and different weights, show that the confidence values usually are higher in Tiny-YOLOv4, which is the network that gave the best results overall--consistently above 30%. he other networks returned more inconsistent values (between 10% and 80%) with more false positives. However, this is not how it evolved when looking at each individual dataset. Sometimes, confidence values increased and decreased from network to network; for example, in Figure 4, the confidence values seem to increase from Tiny-YOLOv3 to Tiny-YOLOv4 but decrease slightly from Tiny-YOLOv2 to Tiny-YOLOv3. This of course is not consistent  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Method** & **Train** & \(AP_{\textbf{50}}\) & \(AP_{\textbf{75}}\) \\ \hline YOLO & RGB & 41.61\% & 2.07\% \\ Tiny-YOLO & RGB & 26.52\% & 0.30\% \\ Tiny-YOLOv2 & RGB & 38.67\% & 6.65\% \\ Tiny-YOLOv3 & RGB & 56.73\% & 11.82\% \\ Tiny-YOLOv4 & RGB & 61.47\% & 19.62 \\ \hline YOLO & CIR & 31.72\% & 2.79\% \\ Tiny-YOLO & CIR & 21.93\% & 0.49\% \\ Tiny-YOLOv2 & CIR & 28.58\% & 1.16\% \\ Tiny-YOLOv3 & CIR & 49.82\% & 4.92\% \\ Tiny-YOLOv4 & CIR & 52.87\% & 12.39\% \\ \hline YOLO & NDRE & 11.10\% & 1.34\% \\ Tiny-YOLO & NDRE & 4.37\% & 0.01\% \\ Tiny-YOLOv2 & NDRE & 16.65\% & 0.76\% \\ Tiny-YOLOv3 & NDRE & 46.20\% & 6.57\% \\ Tiny-YOLOv4 & NDRE & 50.86\% & 11.96\% \\ \hline YOLO & NDWI & 4.42\% & 0.00\% \\ Tiny-YOLO & NDWI & 0.67\% & 0.00\% \\ Tiny-YOLOv2 & NDWI & 17.16\% & 0.57\% \\ Tiny-YOLOv3 & NDWI & 42.97\% & 3.23\% \\ Tiny-YOLOv4 & NDWI & 46.34\% & 4.09\% \\ \hline YOLO & GS & 20.09\% & 0.30\% \\ Tiny-YOLO & GS & 11.99\% & 0.44\% \\ Tiny-YOLOv2 & GS & 14.51\% & 1.89\% \\ Tiny-YOLOv3 & GS & 29.61\% & 8.55\% \\ Tiny-YOLOv4 & GS & 32.28\% & 10.04\% \\ \hline YOLO & NDVI & 21.63\% & 3.33\% \\ Tiny-YOLO & NDVI & 18.61\% & 0.26\% \\ Tiny-YOLOv2 & NDVI & 18.13\% & 0.11\% \\ Tiny-YOLOv3 & NDVI & 27.79\% & 3.00\% \\ Tiny-YOLOv4 & NDVI & 21.25\% & 6.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regression based networks training. \(AP_{\textbf{50}}\): average precision with IoU of 0.5; \(AP_{\textbf{75}}\): average precision with IoU of 0.75; Train: dataset used in training; Method: Network used for training.  
with all detections but an overall observation after looking at a random set of images from the test dataset. The NDRE and NDWI datasets started the worst with none to very few detections in the random set, from Tiny-YOLO and YOLO, which could be expected, since those were not able to achieve an \(AP_{50}\) above 45%. Slight increases in detections were observed with the latter networks, Tiny-YOLOv3 and Tiny-YOLOv4; and at this point, two detections of the same gap became rare unlike with the previous networks. There were not found at any point, in this random test set, situations where the networks categorized a path as a gap, but other errors occurred: as seen in Figure 4, sometimes trees confused the networks into believing there were gaps.