Tiny and Efficient Model for the Edge Detection Generalization (2308.06468v1)

Abstract: Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only $58K$ parameters, less than $0.2$% of the state-of-the-art models. Training on the BIPED dataset takes $less than 30 minutes$, with each epoch requiring $less than 5 minutes$. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED.

• The paper introduces TEED, a model that reduces computational load to only 58K parameters while achieving competitive edge detection accuracy.
• It employs a streamlined CNN architecture with skip-connections and the new Smish activation, along with a double fusion module for crisp edge mapping.
• The model achieves strong performance with ODS of 0.828 and OIS of 0.842 on the UDED benchmark, enabling rapid training and practical image retrieval applications.

An Analytical Overview of "Tiny and Efficient Model for the Edge Detection Generalization"

The paper "Tiny and Efficient Model for the Edge Detection Generalization" introduces a novel approach to edge detection, focusing on reducing model complexity while maintaining robust generalization capabilities. The authors present the Tiny and Efficient Edge Detector (TEED), a Convolutional Neural Network (CNN) architecture notable for its simplicity and efficiency. With only 58,000 parameters, TEED is far leaner than contemporary state-of-the-art edge detection models, which often utilize tens of millions of parameters.

Methodological Approach

TEED is designed around three core principles: simplicity, efficiency, and generalization. The architecture incorporates a minimal yet effective design, using skip-connections and a new activation function, Smish, to enhance training convergence and edge detection quality. The backbone of TEED is constructed using a simple multi-block structure that embraces the advantages of modern deep learning architectures like ResNet, yet remains significantly lightweight.

To improve edge map fusion, the paper introduces a Double Fusion (dfuse) module, drawing inspiration from existing methodologies such as CATS. This fusion module employs depth-wise convolutions (DWConv) to maintain a large receptive field with minimal computational overhead, resulting in crisp and clear edge detections without requiring complex operations like Softmax or group normalization.

Comparative Analysis

The evaluation is conducted on the UDED dataset, a newly proposed benchmark designed to test edge detection across diverse scenes. This dataset is curated to offer a challenging yet practical assessment of edge detector models, particularly in measuring their generalization abilities. Compared to other models trained on popular datasets like BIPED, TEED achieves higher accuracy and efficiency, underlined by its ability to be trained rapidly—completing in less than 30 minutes per epoch—and achieve competitive ODS and OIS scores of 0.828 and 0.842, respectively.

Furthermore, the TEED model is validated in practical applications such as sketch-based image retrieval, further exemplifying its versatility. In comparison to larger models like PiDiNet and BDCN, TEED yields superior performance in both retrieval tasks and in generating artifact-free edge maps, as evidenced by low MSE and high PSNR metrics, indicating the model’s robustness against noise and edge blurring.

Implications and Future Directions

The implications of this work are multifaceted. Practically, the reduced computational cost of TEED allows for deployment in edge devices and scenarios where computational resources are limited. Theoretically, the integration of novel activation functions and lightweight fusion strategies opens avenues for further exploration of efficient architectures in low-level vision tasks.

Future developments can build upon the foundation laid by TEED, particularly in expanding the application scope of such efficient models beyond edge detection, into realms like super-resolution and image enhancement, where low computation and high accuracy are paramount. Exploration into more complex real-world conditions and adaptation to hardware constraints can also offer valuable insights.

In conclusion, this paper contributes significantly to the landscape of edge detection by pioneering a compact model that doesn't compromise on performance. It sets a benchmark in the quest for generalized, efficient, and effective image processing solutions, embodying a pivotal step toward more streamlined and accessible state-of-the-art computer vision applications.