BENet: boundary-enhanced network for real-time semantic segmentation

In the realm of real-time semantic segmentation, deep neural networks have demonstrated promising potential. However, current methods face challenges when it comes to accurately segmenting object boundaries and small objects. This limitation is partly attributed to the prevalence of convolutional neural networks, which often involve multiple sequential down-sampling operations, resulting in the loss of fine-grained details. To overcome this drawback, we introduce BENet, a real-time semantic segmentation network with a focus on enhancing object boundaries. The proposed BENet integrates two key components: the boundary extraction module (BEM) and the boundary adaption layer (BAL). The proposed BEM efficiently extracts boundary information, while the BAL guides the network using this information to preserve intricate details during the feature extraction process. Furthermore, to address the challenges associated with poor segmentation of elongated objects, we introduce the strip mixed aggregation pyramid pooling module (SMAPPM). This module employs strip pooling kernels to effectively expand the contextual representation and receptive field of the network, thereby enhancing overall segmentation performance. Our experiments conducted on a single RTX 3090 GPU show that our method achieves an mIoU of 79.4% at a speed of 45.5 FPS on the Cityscapes test set without ImageNet pre-training.