This research conducts a comparative analysis of Faster R-CNN and YOLOv8 for real-time detection of fishing vessels and fish in maritime surveillance. The study underscores the significance of this investigation in advancing fisheries monitoring and object detection using deep learning. With a clear focus on comparing the performance of Faster R-CNN and YOLOv8, the research aims to elucidate their effectiveness in real-time detection, emphasizing the relevance of such capabilities in fisheries management. By conducting a thorough literature review, the study establishes the current state-of-the-art in object detection, particularly within the context of fisheries monitoring, while discussing existing methods, challenges, and limitations. The findings of this study not only shed light on the superiority of YOLOv8 in precise detection but also highlight its potential impact on maritime surveillance and the protection of marine resources.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11157610 | PMC |
http://dx.doi.org/10.7717/peerj-cs.2033 | DOI Listing |
Front Plant Sci
December 2024
Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, TN, Italy.
Flavescence dorée (FD) poses a significant threat to grapevine health, with the American grapevine leafhopper, , serving as the primary vector. FD is responsible for yield losses and high production costs due to mandatory insecticide treatments, infected plant uprooting, and replanting. Another potential FD vector is the mosaic leafhopper, , commonly found in agroecosystems.
View Article and Find Full Text PDFFront Plant Sci
December 2024
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: Detecting strawberry growth stages is crucial for optimizing production management. Precise monitoring enables farmers to adjust management strategies based on the specific growth needs of strawberries, thereby improving yield and quality. However, dense planting patterns and complex environments within greenhouses present challenges for accurately detecting growth stages.
View Article and Find Full Text PDFFront Plant Sci
December 2024
The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming, Yunnan, China.
Tea leaf diseases are significant causes of reduced quality and yield in tea production. In the Yunnan region, where the climate is suitable for tea cultivation, tea leaf diseases are small, scattered, and vary in scale, making their detection challenging due to complex backgrounds and issues such as occlusion, overlap, and lighting variations. Existing object detection models often struggle to achieve high accuracy in detecting tea leaf diseases.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan.
The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. This signal contains a number of distinctive waveform patterns that reflect the subject's health state in relation to sleep, neurological disorders, memory functions, and more. In this regard, sleep spindles and K-complexes are two major waveform patterns of interest to specialists, who visually inspect the recordings to identify these events.
View Article and Find Full Text PDFBackground: Dengue fever poses a significant global public health concern, necessitating the monitoring of Aedes mosquito population density. These mosquitoes serve as the disease vectors, making their surveillance crucial for dengue prevention. The objective of this study was to address the difficulty associated with identifying and counting mosquito eggs of wild strains during the monitoring of Aedes albopictus (Diptera: Culicidae) density via ovitraps in field surveys.
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