Deep Learning in Medical Image Classification and Object Detection: a Survey

Gupta, Priyanka; Gupta, Shikha

doi:10.37628/ijippr.v8i2.846

Cited by 2 publications

(1 citation statement)

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“…It is difficult to identify smaller objects in an image due to clustering. This architecture has issues with object localization and recognition when the dimensions of the object are different from those used for the training data [40,41]. Owing to localization errors, the main aim of YOLOv1 is to identify objects in a given image, whereas in Faster R-CNN, YOLOv2 [42] introduced the concept of an anchor box to increase the positioning performance.…”

Section: Detection Methodsmentioning

confidence: 99%

Counting Abalone with High Precision Using YOLOv3 and DeepSORT

Kibet,

Shin

2023

Processes

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In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras used to detect abalones. Considering the computational effectiveness and improved detection algorithms, this proposal is promising compared to the previously proposed methods. Some of these methods have low effectiveness and accuracy, and they provide an incorrect counting rate because some of the abalones tend to entangle, resulting in counting two or more abalones as one. Conducting detection and tracking research is crucial to achieve modern solutions for small- and large-scale fishing industries that enable them to accomplish higher automation, non-invasiveness, and low cost. This study is based on the development and improvement of counting analysis tools for automation in the fishing industry. This enhances agility and generates more income without the cost created by inaccuracy.

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Section: Detection Methodsmentioning

confidence: 99%