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_e _e _aAustria, John Marie E. Baligod, Roland carlos S. Erno, Gary Daniel I. Sandico, Pauline R. Sundiam. Ferdinand Francis R. _d _b4 _u _c0 _q16 |
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_a _aPooPatrol: ioT - Integrated dog litter identification and monitoring using machine learning. _d _b _n _c _h6 _p |
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_3 _30 _b _aunmediated _2rdamedia |
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_a _aUndergraduate Thesis : (Bachelor of Science in Computer Engineering) - Pamantasan ng Lungsod ng Maynila ,2024. _d _b _c56 |
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_b _b _c _aABSTRACT: STATEMENT OF THE PROBLEM: The PooPatrol addresses the issue of dog waste pollution in public spaces of Barangay 506. There is currently no effective to detect and monitor dog feces, which leads to lack of information about the places most impacted by dog littering. As accurately detecting the dog litter in images and videos is challenging due to insufficient approach. Moreover, there isn't medium in place to notify the officials about the occurrence of dog littering. The research also includes plans for sustainability and transitioning the system for long-term use by Barangay 506. RESEARCH METHODOLOGY: This study executed a research approach and employed by the Agile methodology within the System Development Life Cycle framework for software and hardware development. The litter detection system was developed using Teachable Machine to reate for both defecating and dog breed detection, trained using TensorFlow in Python. The barangay's Graphical User Interface (GUI) was developed with NodeJS and Flask. Moreover, when dog littering is detected, the system sends an email alert and/or SMS using the GSM Module. Also, extensive testing was conducted to assess the functionalities and effectiveness of the PoolPatrol system. SUMMARY FINDINGS: The system has been deployed for Barangay 506 with the use of advanced technology to handle the common problem of dog waste control and monitoring. It became clear that irresponsible dog ownership and roaming dogs were major causes of the issue after conducting preliminary surveys. Despite obstacles including weather delays and schedule difficulties, PooPatrol was deployed and observed for at least two days. The system's capacity to identify breeds and detect dog waste events was shown by the true positives while, the false positives indicated where the algorithm and alert system needed to improved. Overall, the system's mean average precision of 0.7321, and mean recall of 0.575, indicated good performance but suggested room for enhancement. Training procedures involved datasets, show the system's sensitivity to environmental stimuli and the need for refinement in detecting defecation. Barangay authorities feedback emphasized the sytem's benefits and ease of use which can help to solve their main concerns. CONCLUSION: The PooPatrol soved the main concerns of the Barangay 506 which is the dog littering. With a developed system, the barangay can easily monitor and detect act of dog defecating. The collection of datasets was successfully used to create and train models that can detect defecating poses and dog breeds using machine learning algorithm such as TensorFlow. Once the system detects dog litter through the camera, it will first identify the dog breed then the other model will detect if the dog is defecating by its position. If the dog is indeed defecating, it will generate an alert system to the barangay official. RECOMMENDATION: This study presents recommendations aimed to enhance the effectiveness of the PooPatrol system. Firstly, improving camera proximity and resolution is advised to enhance feces and breed recognition by positioning dogs closer to the camera and use of advanced camera technology. Upgrading hardware specifications with a superior GPU and CPU is recommended to meet computational demands while maintaining optimal performance. Collecting datasets directly from CCTV cameras in the target environment is proposed to familiarize the model with specific conditions, and collecting detailed images us suggested to improve accurate identification. Dataset selection is needed for pattern recognition in dog behaviour. Ensuring a strong signal for the GSM Module or improving connectivity can enhance SMS notification reliability. Research about the NCAP CCTV used by the LTO for much more improved monitoring and enforcement capabilities. Finally, developing PooPatrol as a service similar like with the NCAP CCTV cameras can provide government units with a solution for pet waste management, including measures to deter repeat offenses, enforce penalties, register repeat offenders, and monitor animal theft. The intent of these recommendations is to improve the system's overall performance regarding controlling and monitoring dog waste. _u |
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