{"id":126087,"date":"2024-04-15T18:00:30","date_gmt":"2024-04-15T12:30:30","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=126087"},"modified":"2026-06-01T12:33:46","modified_gmt":"2026-06-01T07:03:46","slug":"pigeon-detection-system-using-opencv","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pigeon-detection-system-using-opencv\/","title":{"rendered":"Python OpenCV Project &#8211; Pigeon Detection System"},"content":{"rendered":"<p>Pigeon detection is the process of identifying and classifying pigeons in digital images or video footage. It plays a crucial role in various applications, including wildlife conservation, urban pest control and surveillance.<\/p>\n<p>Advanced computer vision techniques and deep learning models are used to automatically distinguish pigeons from other objects or animals. Accurate pigeon detection is valuable for managing pigeon populations and understanding their behaviors in different contexts.<\/p>\n<h2>What is ResNet-50<\/h2>\n<p>ResNet-50 is a deep convolutional neural network architecture used in computer vision tasks. It comprises 50 layers and is known for its residual connections, which allow for the training of very deep networks. ResNet-50 has achieved remarkable success in image recognition tasks such as image classification and object detection by mitigating the vanishing gradient problem and improving training efficiency.<\/p>\n<h3>Dataset<\/h3>\n<p>A dataset containing pigeon and not pigeon images is crucial for training a deep learning model to distinguish pigeons from other objects. This labeled dataset provides essential training samples, enabling the development of an accurate pigeon detention system for wildlife monitoring.<\/p>\n<h3>Prerequisites For Python OpenCV Pigeon Detection System<\/h3>\n<p>Proficiency in Python and a solid understanding of the OpenCV library are prerequisites. Additionally, adherence to specified system requirements is necessary for the successful execution of this project.<\/p>\n<ul>\n<li>Python 3.7 (64-bit) and above<\/li>\n<li>Any Python editor (VS code, Pycharm)<\/li>\n<li>GPU 4.00 GB+<\/li>\n<\/ul>\n<p><strong>Note:-<\/strong> In this Tutorial Google Colab is used.<\/p>\n<h3>Download Python OpenCV Pigeon Detection System Project<\/h3>\n<p>Please download the source code of Python OpenCV Pigeon Detection System Project: <a href=\"https:\/\/drive.google.com\/file\/d\/17jTeyFN1uCJkTn0PoAgs_9nDfFcxbxP-\/view?usp=drive_link\"><strong>Python<\/strong> <strong>OpenCV Pigeon Detection System Project Code.<\/strong><\/a><\/p>\n<h3>Installation<\/h3>\n<p>Open windows cmd as administrator<\/p>\n<p>1. Install OpenCV library.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">pip install opencv-python<\/pre>\n<p>2. Install TensorFlow library.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">pip install tensorflow<\/pre>\n<h3>Let\u2019s Implement<\/h3>\n<h4>Model Training<\/h4>\n<p>1. Import all the packages that are required in implementation.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">import tensorflow as tf\r\nfrom tensorflow.keras.applications import ResNet50\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D\r\nfrom tensorflow.keras.models import Model\r\nfrom tensorflow.keras.optimizers import Adam\r\nfrom tensorflow.keras.preprocessing import image\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt<\/pre>\n<p>2. It sets up a deep learning model based on ResNet-50 with specific architecture and pretrained weights. It then freezes the layers in the pertained model to retain their weights.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">batch_size = 32\r\nepochs = 10\r\nimage_size = (224, 224)\r\nMODEL = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\r\nx = MODEL.output\r\nx = GlobalAveragePooling2D()(x)\r\nx = Dense(1024, activation='relu')(x)\r\npredictions = Dense(1, activation='sigmoid')(x)\r\nmodel = Model(inputs=MODEL.input, outputs=predictions)\r\nfor layer in MODEL.layers:\r\n    layer.trainable = False<\/pre>\n<p>3. It configures an image and data generator with various data augmentation techniques. It generates batches from a directory of images for binary classification.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">train_datagen = ImageDataGenerator(\r\n    rescale=1.0 \/ 255.0,\r\n    rotation_range=30,\r\n    width_shift_range=0.2,\r\n    height_shift_range=0.2,\r\n    shear_range=0.2,\r\n    zoom_range=0.2,\r\n    horizontal_flip=True,\r\n    fill_mode='nearest'\r\n)\r\n\r\ntrain_generator = train_datagen.flow_from_directory(\r\n    '\/content\/train',\r\n    target_size=image_size,\r\n    batch_size=batch_size,\r\n    class_mode='binary'\r\n)<\/pre>\n<p>4. It compiles the model using the Adam optimizer with specified parameters. Then it trains the model and prints the accuracy.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\r\nmodel.fit(\r\n    train_generator,\r\n    steps_per_epoch=train_generator.samples \/\/ batch_size,\r\n    epochs=epochs\r\n)\r\naccuracy = model.evaluate(train_generator, return_dict=True)['accuracy']\r\nprint(f'Model Accuracy: {accuracy * 100:.2f}%')<\/pre>\n<p><strong>Output of this step:<\/strong><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/adam-optimizer-with-specified.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132248 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/adam-optimizer-with-specified.webp\" alt=\"adam optimizer with specified\" width=\"1719\" height=\"682\" \/><\/a><\/p>\n<p>5. Once the model is trained, it saves the trained model.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model.save('pigeon_detection_model.h5')<\/pre>\n<h4>Model Testing<\/h4>\n<p>6. It loads the trained model for pigeon detection and displays the image with the prediction result.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\">model = tf.keras.models.load_model('pigeon_detection_model.h5')\r\ndef predict_image_class(image_path, model):\r\n    IMAGE = image.load_img(image_path, target_size=(224, 224))\r\n    IMAGE = image.img_to_array(IMAGE)\r\n    IMAGE = np.expand_dims(IMAGE, axis=0)\r\n    IMAGE = IMAGE \/ 255.0 \r\n    prediction = model.predict(IMAGE)\r\n    return prediction[0][0]  \r\n\r\nimage_path = '\/content\/train\/Not Pigeon\/142.jpg'\r\nprediction = predict_image_class(image_path, model)\r\nIMAGE = image.load_img(image_path)\r\nplt.imshow(IMAGE)\r\nplt.title(f'Prediction: {\"Pigeon\" if prediction &gt; 0.5 else \"Not Pigeon\"}')\r\nplt.show()\r\nprint(prediction)\r\nif prediction &gt; 0.5:\r\n    print(\"This is a pigeon.\")\r\nelse:\r\n    print(\"This is not a pigeon.\")<\/pre>\n<h3>Python OpenCV Pigeon Detection System Output<\/h3>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-pigeon-detection-system.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132249 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-pigeon-detection-system.webp\" alt=\"opencv pigeon detection system\" width=\"709\" height=\"700\" \/><\/a><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-pigeon-detection-system-output.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132250 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-pigeon-detection-system-output.webp\" alt=\"opencv pigeon detection system output\" width=\"631\" height=\"679\" \/><\/a><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/pigeon-detection-system.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132251 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/pigeon-detection-system.webp\" alt=\"pigeon detection system\" width=\"655\" height=\"682\" \/><\/a><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/pigeon-detection-system-opencv-output.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-132252 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/pigeon-detection-system-opencv-output.webp\" alt=\"pigeon detection system opencv output\" width=\"682\" height=\"684\" \/><\/a><\/p>\n<h3>Conclusion<\/h3>\n<p>In conclusion, the development of pigeon detection technology holds great promise in addressing urban challenges and improving public spaces. By leveraging advanced computer vision techniques, deep learning models and real-time monitoring systems, we can effectively manage and mitigate issues associated with pigeons, such as sanitation and safety concerns.<\/p>\n<p>This innovative solution not only enhances the quality of urban life but also exemplifies the potential of technology to harmonize the coexistence of wildlife and humans in our ever-evolving cities.<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:2542,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/17jTeyFN1uCJkTn0PoAgs_9nDfFcxbxP-\\\/view?usp=drive_link&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20260601070305\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/17jTeyFN1uCJkTn0PoAgs_9nDfFcxbxP-\\\/view?usp=drive_link&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2026-06-02 09:35:55&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-06 11:21:38&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-21 12:20:43&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-21 12:20:43&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Pigeon detection is the process of identifying and classifying pigeons in digital images or video footage. It plays a crucial role in various applications, including wildlife conservation, urban pest control and surveillance. Advanced computer&#46;&#46;&#46;<\/p>\n","protected":false},"author":86671,"featured_media":132254,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[27755],"tags":[30180,30181,21719,30129,30121,30184,30182,30183,30118],"class_list":["post-126087","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-opencv-tutorials","tag-opencv-pigeon-detection-system","tag-opencv-pigeon-detection-system-project","tag-opencv-projects","tag-opencv-projects-for-practice","tag-opencv-projects-ideas","tag-pigeon-detection-project","tag-pigeon-detection-system","tag-pigeon-detection-system-using-opencv","tag-python-opencv-projects"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Python OpenCV Project - Pigeon Detection System - DataFlair<\/title>\n<meta name=\"description\" content=\"Advanced computer vision techniques and deep learning models are used to automatically distinguish pigeons from other objects or animals.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/pigeon-detection-system-using-opencv\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python OpenCV Project - Pigeon Detection System - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Advanced computer vision techniques and deep learning models are used to automatically distinguish pigeons from other objects or animals.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/pigeon-detection-system-using-opencv\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-15T12:30:30+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-01T07:03:46+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-pigeon-detection-system-1.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"TechVidvan Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"TechVidvan Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Python OpenCV Project - Pigeon Detection System - DataFlair","description":"Advanced computer vision techniques and deep learning models are used to automatically distinguish pigeons from other objects or animals.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/pigeon-detection-system-using-opencv\/","og_locale":"en_US","og_type":"article","og_title":"Python OpenCV Project - Pigeon Detection System - DataFlair","og_description":"Advanced computer vision techniques and deep learning models are used to automatically distinguish pigeons from other objects or animals.","og_url":"https:\/\/data-flair.training\/blogs\/pigeon-detection-system-using-opencv\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2024-04-15T12:30:30+00:00","article_modified_time":"2026-06-01T07:03:46+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2023\/12\/opencv-pigeon-detection-system-1.webp","type":"image\/webp"}],"author":"TechVidvan Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"TechVidvan Team","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/pigeon-detection-system-using-opencv\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/pigeon-detection-system-using-opencv\/"},"author":{"name":"TechVidvan Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/0e594f928e31fc96628ac40f6ae74f49"},"headline":"Python OpenCV Project &#8211; 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