Automatic detection of traffic signs is crucial for Advanced Driving Assistance Systems (ADAS). Current two-stage approaches consist of a preliminary object detection step, where the traffic signs are categorized within broader families (e.g., speed limits), and then sub-classes (e.g., speed limit 40). However, these cascading methods fail to achieve satisfying performance, especially in more realistic driving scenarios where images are acquired under more challenging conditions. Under such conditions, the first-stage detection step is likely to provide inaccurate predictions, making the subsequent classification step useless. In this paper, we propose a simple yet effective two-stage fusion framework for traffic sign detection. Different from the previous cascading method, our framework directly predicts categories in the first-stage detection and fuse the two-stage category predictions to improves overall robustness. Besides, in order to filter the false detection boxes under low-resolution inputs, we also propose an effective post-processing method called Surrounding-Aware Non-Maximum Suppression (SA-NMS) as an alternative technique for the first-stage detection. After combining the above proposed methods, our framework obtains good detection performance. Experimental results on the widely used Tsinghua-Tencent 100K (TT100K) traffic sign dataset, which contains images of traffic signs collected under a variety of challenging conditions, show that the proposed framework outperforms current approaches in both accuracy and inference speed, achieving 89.7 mAP and 65 FPS for ${608\times608}$ low resolution images.

Transformer-based deep learning networks are revolutionizing our society. The convolution and attention co-designed (CAC) Transformers have demonstrated superior performance compared to the conventional Transformer-based networks. However, CAC Transformer networks contain various nonlinear functions, such as softmax and complex activation functions, which require high precision hardware design yet typically with significant cost in area and power consumption. To address these challenges, SoftAct , a compact and high-precision algorithm-hardware co-designed architecture, is proposed to implement both softmax and nonlinear activation functions in CAC Transformer accelerators. An improved softmax algorithm with penalties is proposed to maintain precision in hardware. A stage-wise full zero detection method is developed to skip redundant computation in softmax. A compact and reconfigurable architecture with a symmetrically designed linear fitting module is proposed to achieve nonlinear functions. The SoftAct architecture is designed in an industrial 28-nm CMOS technology with the MobileViT-xxs network classifying the ImageNet-1k dataset as the benchmark. Compared with the state of the art, SoftAct improves up to 5.87% network accuracy under 8-bit quantization, 153.2× area efficiency, and 1435× overall efficiency.

Various algorithms based on Convolutional Neural Network (CNN) have achieved great performance in the task of face anti-spoofing (FAS). However, the issue of most approaches is that the robustness in unknown scenes is not strong due to the different quality of attack images and environmental factors. In this letter, we propose a real-time face anti-spoofing method based on stereo matching. We input the left and right views of a pair of infrared face images into our proposed lightweight stereo matching network to get a disparity map. Then, we use the disparity map as input for the classification network to obtain face living information. Experimental results show that the proposed approach has significantly improved performance compared with the latest state-of-the-art face anti-spoofing methods. Considering the real-time requirement, our method is superior to most single-image-based models in inference time and far less than those based on other large scale stereo matching networks in computational complexity. The code is available at https://github.com/lizhishan1997/StereoMatching_FAS.

Object detection is one of the essential tasks in computer vision, with most detection methods relying on a limited number of sizes for anchor boxes. However, the boundaries of particular composite objects, such as ports, highways, and golf courses, are ambiguous in remote sensing images, and therefore, it is challenging for the anchor-based method to accommodate the substantial size variation of the objects. In addition, the dense placement of anchor boxes imbalances the positive and negative samples, which affects the end-to-end architecture of deep learning methods. Hence, this paper proposes a single-stage object detection model named Xnet to address this issue. The proposed method designs a deformable convolution backbone network used in the feature extraction stage. Compared to the standard convolution, it adds learnable parameters for dynamically analyzing the boundary and offset of the receptive field, rendering the model more adaptable to size variations within the same class. Moreover, this paper presents a novel anchor-free detector that classifies objects in feature images point-by-point, without relying on anchor boxes. Several experiments on the large remote sensing dataset DIOR challenging Xnet against other popular methods demonstrate that our method attains the best performance, surpassing by 4.7% on the mAP (mean average precision) metric.

An energy-efficient convolutional neural network (CNN) accelerator is proposed for low-power inference on edge devices. An adaptive zero skipping technique is proposed to dynamically skip the zeros in either activations or weights, depending on which has the higher sparsity. The characteristic of non-zero data aggregation is explored to enhance the effectiveness of adaptive zero skipping in performance boosting. To mitigate the load imbalance issue after zero skipping, a sparsity-driven data flow and low-complexity dynamic task allocation are employed for different convolution layers. Facilitated further by a two-stage distiller, the proposed accelerator achieves 5.42×, 3.41×, and 3.42× performance boosting for VGG16, AlexNet, and Mobilenet-v1, respectively, compared to the baseline. Implemented in a 55-nm low power CMOS technology, the proposed accelerator achieves an effective energy efficiency of 2.41 TOPS/W, 2.35 TOPS/W, and 0.64 TOPS/W for VGG16, AlexNet, and Mobilenet-v1, respectively, at 100 MHz and 1.08 V supply voltage.

Three-dimensional (3D) understanding or inference has received increasing attention, where 3D convolutional neural networks (3D-CNNs) have demonstrated superior performance compared to two-dimensional CNNs (2D-CNNs), since 3D-CNNs learn features from all three dimensions. However, 3D-CNNs suffer from intensive computation and data movement. In this paper, Sagitta, an energy-efficient low-latency on-chip 3D-CNN accelerator, is proposed for edge devices. Locality and small differential value dropout are leveraged to increase the sparsity of activations. A full-zero-skipping convolutional microarchitecture is proposed to fully utilize the sparsity of weights and activations. A hierarchical load-balancing scheme is also introduced to increase the hardware utilization. Specialized architecture and computation flow are proposed to enhance the effectiveness of the proposed techniques. Fabricated in a 55-nm CMOS technology, Sagitta achieves 3.8 TOPS/W for C3D at a latency of 0.1 s and 4.5 TOPS/W for 3D U-Net at a latency of 0.9 s at 100 MHz and 0.91 V supply voltage. Compared to the state-of-the-art 3D-CNN and 2D-CNN accelerators, Sagitta enhances the energy efficiency by up to 379.6× and 11×, respectively.