Browsing by Subject "semantic segmentation"

Now showing 1 - 4 of 4
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    Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations
    (JST, 2020) Sulistiyo, Mahmud Dwi; Kawanishi, Yasutomo; Deguchi, Daisuke; Ide, Ichiro; Hirayama, Takatsugu; Zheng, Jiang-Yu; Murase, Hiroshi; Computer and Information Science, School of Science
    Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object's class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task.
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    Deep Learning based Crop Row Detection with Online Domain Adaptation
    (ACM, 2021-08) Doha, Rashed; Al Hasan, Mohammad; Anwar, Sohel; Rajendran, Veera; Computer and Information Science, School of Science
    Detecting crop rows from video frames in real time is a fundamental challenge in the field of precision agriculture. Deep learning based semantic segmentation method, namely U-net, although successful in many tasks related to precision agriculture, performs poorly for solving this task. The reasons include paucity of large scale labeled datasets in this domain, diversity in crops, and the diversity of appearance of the same crops at various stages of their growth. In this work, we discuss the development of a practical real-life crop row detection system in collaboration with an agricultural sprayer company. Our proposed method takes the output of semantic segmentation using U-net, and then apply a clustering based probabilistic temporal calibration which can adapt to different fields and crops without the need for retraining the network. Experimental results validate that our method can be used for both refining the results of the U-net to reduce errors and also for frame interpolation of the input video stream.
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    SE3: Sequential Semantic Segmentation of Large Images with Minimized Memory
    (IEEE, 2022-08) Cheng, Guo; Zheng, Jiang Yu; Computer and Information Science, School of Science
    Semantic segmentation results in pixel-wise perception accompanied with GPU computation and expensive memory, which makes trained models hard to apply to small devices in testing. Assuming the availability of hardware in training CNN backbones, this work converts them to a linear architecture enabling the inference on edge devices. Keeping the same accuracy as patch-mode testing, we segment images using a scanning line with the minimum memory. Exploring periods of pyramid network shifting on image, we perform such sequential semantic segmentation (SE3) with a circular memory to avoid redundant computation and preserve the same receptive field as patches for spatial dependency. In the experiments on large drone images and panoramas, we examine this approach in terms of accuracy, parameter memory, and testing speed. Benchmark evaluations demonstrate that, with only one-line computation in linear time, our designed SE3 network consumes a small fraction of memory to maintain an equivalent accuracy as the image segmentation in patches. Considering semantic segmentation for high-resolution images, particularly for data streamed from sensors, this method is significant to the real-time applications of CNN based networks on light-weighted edge devices.
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    Sequential Semantic Segmentation of Road Profiles for Path and Speed Planning
    (IEEE, 2022-12) Cheng, Guo; Yu Zheng, Jiang; Computer and Information Science, School of Science
    Driving video is available from in-car camera for road detection and collision avoidance. However, consecutive video frames in a large volume have redundant scene coverage during vehicle motion, which hampers real-time perception in autonomous driving. This work utilizes compact road profiles (RP) and motion profiles (MP) to identify path regions and dynamic objects, which drastically reduces video data to a lower dimension and increases sensing rate. To avoid collision in a close range and navigate a vehicle in middle and far ranges, several RP/MPs are scanned continuously from different depths for vehicle path planning. We train deep network to implement semantic segmentation of RP in the spatial-temporal domain, in which we further propose a temporally shifting memory for online testing. It sequentially segments every incoming line without latency by referring to a temporal window. In streaming-mode, our method generates real-time output of road, roadsides, vehicles, pedestrians, etc. at discrete depths for path planning and speed control. We have experimented our method on naturalistic driving videos under various weather and illumination conditions. It reached the highest efficiency with the least amount of data.