The growing number of edge devices in everyday life generates a considerable amount of data that current AI algorithms, like artificial neural networks, cannot handle inside edge devices with limited bandwidth, memory, and energy available. Neuromorphic computing, with low-power Oscillatory Neural Networks (ONNs), is an alternative and attractive solution to solve complex problems at the edge. However, ONN is currently limited with its fully-connected recurrent architecture to solve auto-associative memory problems. In this work, we use an alternative 2-layer bidirectional ONN architecture. We introduce a 2-layer feedforward ONN architecture to perform image edge detection, using the ONN to replace convolutional filters to scan the image. Using an HNN Matlab emulator and digital ONN design simulations, we report efficient image edge detection from both architectures using various size filters (3x3, 5x5, and 7x7) on black and white images. In contrast, the feedforward architectures can also perform image edge detection on gray scale images. With the digital ONN design, we also assess latency performances and obtain that the bidirectional architecture with a 3x3 filter size can perform image edge detection in real-time (camera flow from 25 to 30 images per second) on images with up to 128x128 pixels while the feedforward architecture with same 3x3 filter size can deal with 170x170 pixels, due to its faster computation.