Dataset Information

Object detection in the ring scotoma of a monocular bioptic telescope.

ABSTRACT: OBJECTIVE:To evaluate the ability of the fellow eye to detect stimuli in the area corresponding to the ring scotoma (blind area) of a monocular bioptic telescope in simple conditions (conventional perimetry) and in more visually demanding conditions. METHODS:A computerized dichoptic perimeter enabled separate stimuli to be presented to each eye of 7 bioptic users and 7 nonusers. The bioptic ring scotoma was mapped by presenting the stimulus to the telescope eye only. Detection tests were then conducted under binocular viewing, with stimuli presented only to the fellow eye in a 2 × 2 × 2 design with or without telescope, on plain gray or patterned (spatial noise) background, and with passive (looking at cross) or active (reading letters) fixation task. RESULTS:No significant differences were noted in fellow-eye detection with (86%) and without (87%) a bioptic. The detection rate was significantly reduced on the patterned background and in the active fixation task. CONCLUSIONS:To our knowledge, this is the first study to demonstrate fellow-eye detection in the area of the ring scotoma with a monocular bioptic telescope under more realistic and visually demanding conditions than conventional perimetry. These results should ease the concern that the monocular ring scotoma might cause blindness to traffic outside the field of the telescope.

SUBMITTER: Doherty AL

PROVIDER: S-EPMC3092125 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Object detection in the ring scotoma of a monocular bioptic telescope.

Doherty Amy L AL Bowers Alex R AR Luo Gang G Peli Eli E

Archives of ophthalmology (Chicago, Ill. : 1960) 20110501 5

<h4>Objective</h4>To evaluate the ability of the fellow eye to detect stimuli in the area corresponding to the ring scotoma (blind area) of a monocular bioptic telescope in simple conditions (conventional perimetry) and in more visually demanding conditions.<h4>Methods</h4>A computerized dichoptic perimeter enabled separate stimuli to be presented to each eye of 7 bioptic users and 7 nonusers. The bioptic ring scotoma was mapped by presenting the stimulus to the telescope eye only. Detection tes ...[more]

PMID: 21555615

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Project description:Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model's inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model's ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling-Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.

Dataset Information

Object detection in the ring scotoma of a monocular bioptic telescope.

Publications

Object detection in the ring scotoma of a monocular bioptic telescope.

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