An Inter-Comparative Survey on State-of-the-Art Detectors—R-CNN, YOLO, and SSD

Sree, B. Bhavya; Bharadwaj, Varun; Neelima, N.

doi:10.1007/978-981-33-4443-3_46

Cited by 18 publications

(5 citation statements)

References 3 publications

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“…SSD has several advantages over other object detection algorithms, including its ability to detect objects at different scales and aspect ratios, its ability to process images quickly, and its ability to detect small objects. However, SSD may not be as accurate as two-stage detectors like Faster R-CNN, especially for detecting small objects or objects with complex shapes [40].…”

Section: Comparison With Previous Approachesmentioning

confidence: 99%

Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection

Mahmud,

Kabir,

Shin

et al. 2023

IEEE Access

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Train incidents with animals and even humans have gotten more attention in the past. Every year, many animals are killed on train tracks, causing an imbalance of the ecosystem and significant delays in railway traffic. Similarly, sometimes train accident is prevalent at the rail gates, where motor vehicles are crossing the train line. All that happens due to the lack of detecting the objects correctly on the train line. Since the detection depends on the driver or human, there is a possibility of occasionally making an error in honking the horn at the right moment, leading to the accident. That's why there needs to be an automated solution to detect the objects on the train line and inform the driver about them. In this research, we proposed a system that detects objects only on the train line. Sometimes, there is an object just beside the train line, which is at a safe distance. To eliminate this kind of wrong detection, we detected the train line first and then detected the objects on the detected train line. Here, we used the Mask R-CNN algorithm to detect the train line and things on the train line. A railway traffic dataset with an input size of 512 x 512 pixels was used to test the proposed methodology, which came up with results with a mean average precision of 0.9375 and a frame rate of 30 frames per second. According to the findings of the experiments, the suggested approach can apply to identify objects on railroads in the real world.

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Section: Comparison With Previous Approachesmentioning

confidence: 99%

Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection

Mahmud,

Kabir,

Shin

et al. 2023

IEEE Access

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“…Other great reviews include [8][9][10]. However, the review from [8] covers until YOLOv3, and [9] covers until YOLOv4, leaving behind the most recent developments.…”

Section: Introductionmentioning

confidence: 99%

“…Other great reviews include [8][9][10]. However, the review from [8] covers until YOLOv3, and [9] covers until YOLOv4, leaving behind the most recent developments. Our paper, different from [10], shows in-depth architectures for most YOLO architectures presented and covers other variations, such as YOLOX, PP-YOLOs, YOLO with transformers, and YOLO-NAS.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

Terven,

Córdova-Esparza,

Romero-González

2023

MAKE

621

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YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO’s development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.

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“…Further, the output layer of the model could be modified to form a bounding box regression (81). Utilizing this property with the You Only Look Once (YOLO) algorithm can be used for the detection of fishes and identifying species at a large scale (14). YOLO works by splitting the image into multiple cells, running the classification model for each such cell, and counting the probability of occurrence; therefore, there exists scope for cross-validating the bounding-box regression with the detected category for better detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

Adhikary,

Banerjee,

Singh

et al. 2024

Preprint

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An intelligent detection and recognition model for the fish species from camera footage is urgently required as fishery contributes to a large portion of the world economy, and these kinds of advanced models can aid fishermen on a large scale. Such models incorporating a pick-and-place machine can be beneficial to sorting different fish species in bulk without human intervention, significantly reducing costs for large-scale fishing industries. Existing methods for detecting and recognizing fish species have many limitations, such as limited scalability, detection accuracy, failure to detect multiple species, degraded performance at a lower resolution, or pinpointing the exact location of the fish. Modifying the head of a compelling deep learning model, namely VGG-16, with pre-trained weights, can be used to detect both the species of the fish and find the exact location of the fish in an image by implementing a modified YOLO to incorporate the bounding box regression head. We have proposed using the ESRGAN algorithm and the proposed neural network to amplify the image resolution by a factor of 4. With this method, an overall detection accuracy of 96.5% has been obtained. The experiment has been conducted based on a total of 9460 images spread across 9 species. After further improving the model, a pick-and-place machine could be integrated to quickly sort the fish according to their species in different large-scale fish industries.

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An Inter-Comparative Survey on State-of-the-Art Detectors—R-CNN, YOLO, and SSD

Cited by 18 publications

References 3 publications

Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection

Advancing Wildlife Protection: Mask R-CNN for Rail Track Identification and Unwanted Object Detection

A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

Fish Species Identification on Low Resolution - A Study with Enhanced Super Resolution Generative Adversarial Network (ESRGAN), YOLO and VGG-16

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