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amc:ss2025:group-e:start [2025/07/29 17:07] – [Discussion & Conclusion] 34602_students.hsrwamc:ss2025:group-e:start [2025/07/29 17:23] (current) – 35983_students.hsrw
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 For our AMC project, we developed a basic traffic monitoring system using a Raspberry Pi 5, designed to detect and count vehicles as they pass through a designated section of road captured by a Pi camera. The collected data is uploaded to a web-based dashboard, which could provide a foundation for traffic flow analysis and supporting studies on air pollution, noise levels, and overall urban livability. Such information can also play a valuable role in infrastructure planning and broader environmental monitoring initiatives. For our AMC project, we developed a basic traffic monitoring system using a Raspberry Pi 5, designed to detect and count vehicles as they pass through a designated section of road captured by a Pi camera. The collected data is uploaded to a web-based dashboard, which could provide a foundation for traffic flow analysis and supporting studies on air pollution, noise levels, and overall urban livability. Such information can also play a valuable role in infrastructure planning and broader environmental monitoring initiatives.
 ===== Material & Methods ===== ===== Material & Methods =====
 +''by Nicole Alvarado (35983) & Ben Koellner (34603)''\\
 ==== Hardware Components ==== ==== Hardware Components ====
-''by Nicole Alvarado (35983) & Ben Koellner (34603)''\\ 
- 
 The project was developed using the following hardware components: The project was developed using the following hardware components:
   * Raspberry Pi 5    * Raspberry Pi 5 
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 ==== Files Setup and Execution === ==== Files Setup and Execution ===
 Provided below is a downloadable ZIP containing the pre-trained COCO object detection model used in this setup. It also includes a list of all detectable object classes that the COCO library has been trained to recognize. All project files, including the object detection, dashboard scripts and vehicle log, were placed into the previously downloaded archives (Object_Detection_Files) and transferred to the Raspberry Pi Desktop using a USB stick. Provided below is a downloadable ZIP containing the pre-trained COCO object detection model used in this setup. It also includes a list of all detectable object classes that the COCO library has been trained to recognize. All project files, including the object detection, dashboard scripts and vehicle log, were placed into the previously downloaded archives (Object_Detection_Files) and transferred to the Raspberry Pi Desktop using a USB stick.
 +
 {{ :amc:ss2025:group-e:object_detection_files.zip |}} {{ :amc:ss2025:group-e:object_detection_files.zip |}}
 +
 To execute the object detection script, enter the following command in the terminal. To execute the object detection script, enter the following command in the terminal.
 <code C++> <code C++>
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 ==== Code ==== ==== Code ====
-Three Python scripts were developed as part of the system's functionality. +Three files were developed as part of the system's functionality: two Python scripts and one CSV file for data logging:
   - Object Detection   - Object Detection
   - Vehicle Log   - Vehicle Log
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 # === Load class names === # === Load class names ===
 classNames = [] classNames = []
-classFile = "/home/aless/Desktop/Object_Detection_Files/coco.names"+classFile = "/home/pi/Desktop/Object_Detection_Files/coco.names"
 with open(classFile, "rt") as f: with open(classFile, "rt") as f:
     classNames = f.read().rstrip("\n").split("\n")     classNames = f.read().rstrip("\n").split("\n")
  
 # === Load model config and weights === # === Load model config and weights ===
-configPath = "/home/aless/Desktop/Object_Detection_Files/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt" +configPath = "/home/pi/Desktop/Object_Detection_Files/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt" 
-weightsPath = "/home/aless/Desktop/Object_Detection_Files/frozen_inference_graph.pb"+weightsPath = "/home/pi/Desktop/Object_Detection_Files/frozen_inference_graph.pb"
  
 assert os.path.exists(classFile), f"Missing file: {classFile}" assert os.path.exists(classFile), f"Missing file: {classFile}"
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 Despite these challenges, the project demonstrates that a low-cost and accessible traffic monitoring solution is possible without specialized AI hardware. With further development, such a system could be adapted for real-world use cases in traffic analysis, planning, or environmental studies. Despite these challenges, the project demonstrates that a low-cost and accessible traffic monitoring solution is possible without specialized AI hardware. With further development, such a system could be adapted for real-world use cases in traffic analysis, planning, or environmental studies.
 ===== Video ===== ===== Video =====
 +{{ :amc:ss2025:group-e:carpi_video.mp4 |}}
  
 ===== References ===== ===== References =====
--insert info +COCO - Common Objects in Context. (n.d). https://cocodataset.org/#home
  
  
amc/ss2025/group-e/start.1753801676.txt.gz · Last modified: 2025/07/29 17:07 by 34602_students.hsrw