ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation (1803.06815v3)

Abstract: We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the state-of-the-art semantic segmentation network PSPNet, while its category-wise accuracy is only 8% less. We evaluated ESPNet on a variety of semantic segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy whole slide image dataset. Under the same constraints on memory and computation, ESPNet outperforms all the current efficient CNN networks such as MobileNet, ShuffleNet, and ENet on both standard metrics and our newly introduced performance metrics that measure efficiency on edge devices. Our network can process high resolution images at a rate of 112 and 9 frames per second on a standard GPU and edge device, respectively.

• The paper introduces the ESP module that factorizes convolutions into point-wise and dilated components to optimize efficiency and accuracy.
• The network uses hierarchical feature fusion to combine multi-scale features and mitigate the gridding artifacts of dilated convolutions.
• The study demonstrates ESPNet’s practical impact, achieving up to 112 fps on GPUs and significant model size reduction compared to PSPNet.

A Review of ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

The paper by Mehta et al. presents ESPNet, an efficient convolutional neural network (CNN) designed explicitly for semantic segmentation of high-resolution images in resource-constrained environments. The crux of ESPNet is its novel convolutional module termed the Efficient Spatial Pyramid (ESP) module, which effectively balances computational efficiency and accuracy on edge devices.

Overview and Contributions

ESPNet addresses the proliferation of computational demands in deep learning models designed for visual segmentation, particularly on edge devices with stringent limitations on memory, power, and computational capacity. The authors introduce a method that exploits the convolution factorization principle to reduce the complexity of CNN operations. By decomposing standard convolutions into point-wise convolutions and a spatial pyramid of dilated convolutions, the ESP modules achieve a significant reduction in parameters and memory usage while maintaining a large effective receptive field. This factorization methodology allows ESPNet to outstrip existing models like MobileNet, ShuffleNet, and ENet in efficiency, making it particularly suitable for deployment in mobile and edge computing environments.

Technical Analysis

The ESP module forms the backbone of ESPNet through an integration of (i) point-wise convolutions for dimensionality reduction, and (ii) spatial pyramid of dilated convolutions for feature map resampling. This dual-step factorization is computationally light, allowing ESPNet to process high-resolution images at impressive rates—112 frames per second on a standard GPU and 9 frames per second on an edge device.

Hierarchical Feature Fusion (HFF) is leveraged within ESP to mitigate the gridding artifacts typically introduced by dilated convolutions. By hierarchically summing features from different dilation rates before concatenation, ESPNet’s architecture ensures more robust feature learning without additional computational burden.

Performance and Evaluation

The authors deploy ESPNet across diverse semantic segmentation datasets, namely Cityscapes, PASCAL VOC, and images from breast biopsy slides. Despite being substantially smaller—22 times faster and 180 times smaller than the benchmark PSPNet—ESPNet achieves commendable category-wise accuracy that is only 8% less. The substantial speedup and reduced memory footprint highlight the potential of ESPNet for real-time applications in autonomous vehicles, drones, and portable medical devices, where rapid local processing is paramount.

Moreover, the paper introduces new performance metrics to gauge CNN efficiency on edge devices, underscoring ESPNet’s architectural suitability for environments demanding real-time data processing with minimal resource consumption.

Future Implications

This work opens a pathway for further advances where CNNs can be optimized not only for accuracy but for effective deployment in resource-constrained settings. Future developments could explore integrating network compression and lower-bit quantization techniques to further enhance ESPNet’s applicability in more demanding contexts without sacrificing performance. Additionally, expansions into other domains such as object detection and instance segmentation could extend the utility of the ESP module's foundational concepts.

In conclusion, ESPNet represents a compelling example of how ingenuity in architectural design can achieve a harmonious trade-off between efficiency and performance. This research adds a significant dimension to the discourse on semantic segmentation, emphasizing efficiency in deep learning models tailored for edge computing applications.