## 5 Results and Discussion
The neuromorphic Canny edge detection network implemented in the present work uses 1 neuron/pixel with the layers arranged in a sequential fashion and introduces a Gaussian kernel in the first layer. This indicates a significant improvement compared to the state-of-the art implementation of the spiking edge detector based on an architecture with a parallel arrangement of Gabor filters, which require six neurons/pixel with the filtering layer [22]. Both works use Hodgkin-Huxley neurons for bio-inspired simulation as a common feature. Figure 3: A comparison of the ground truth with the results from Canny edge detector and the SNN analogue of Canny edge detector for selected images from BSD500 dataset. To compare the edge detectors with the ground truth, we define the performance ratio (PR) as our first metric [35]:  
\[PR=\frac{True\;Edges}{False\;Edges+False\;Non-Edges} \tag{6}\]  
where _True Edges_ are edge pixels identified as edges, _False Edges_ are non-edge pixels identified as edges, and _False Non-Edges_ are edge pixels identified as non-edges. For PR, 0 is the worst score, and \(\infty\) is the best score. Our second metric is \(F_{1}\) score or F-measure, defined as  
\[F_{1}=\frac{2 TP}{(2 TP+FP+FN)}\]  
where _TP_ is true positives, _FP_ is false positives and _FN_ is false negatives. For \(F_{1}\), 0 is the worst score and 1 is the best score. Note that \(F_{1}\) is the same as the Dice similarity coefficient and it can be expressed as \(F_{1}=2J/(1+J)\), where J is the Jaccard similarity index. Table 1 compares the metrics PR and \(F_{1}\)-score of the Canny detector and the neuromorphic Canny detector for the set of images displayed in Figure 3. This table also compares the two metrics for three additional images. The ground truth, Canny edge and SNN Canny edge features of these later three images are displayed for a comparison in Figure 4. We find that the \(F_{1}\)-score for the neuromorphic Canny detector is higher than the Canny detector for all seven images. The SNN-based Canny detector has a relatively higher PR metric than the Canny detector for the images from Figure 3 which corresponds to the top four entrees in Table 1 and a relatively lower PR metric for the images in Figure 4. A detailed observation of the edge maps of the SNN based Canny detector in Figure 4 shows several edge features that appear in the background portion of the image, mostly associated with the variation in the intensity due to texture. These additional features can be helpful to characterize the type of background in the images, and may have an advantage over conventional edge detectors for object detection in edge maps using deep learning. Object detection in infrared images is another key application for edge detectors, for example in night vision gadgets or for autonomous driving. Novel edge detectors, especially those that can extract edges in low-resolution infrared images with low-SWaP  
Figure 4: A comparison of the ground truth with the results from Canny edge detector and the SNN analogue of Canny edge detector for selected images from BSD500 dataset. For these images, the metrics for SNN Canny edge detector come out lower compared to the Canny edge detector. requirements for hardware implementation, are sought across commercial and defense applications. In this context, we evaluated our SNN edge detector with a few infrared images from the Thermal Road Dataset [36] (license: Not found). Figure 5 shows a comparison of edge detection with SNN-based Canny detector, conventional Canny detector and Sobel detector. Ground truth edge maps are not available for this dataset to perform a quantitative comparison similar to the one presented for the RGB images in Table 1. A visual comparison of the results from the three edge detectors in Figure 5 indicates that the SNN-based Canny edge detector is able to generate edge maps very similar to the ones generated by the conventional Canny edge detector.