YOLO(You Only Look Once)\ub294 \uac1d\uccb4 \uac10\uc9c0 \ubd84\uc57c\uc5d0\uc11c \uac00\uc7a5 \uc778\uae30 \uc788\ub294 \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. YOLO\ub294 \ub2e8\uc77c \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \ub0b4\uc5d0\uc11c \uac1d\uccb4\ub97c \uc2e4\uc2dc\uac04\uc73c\ub85c \uac10\uc9c0\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 YOLO\ub97c \ud65c\uc6a9\ud558\uc5ec \uba40\ud2f0\ud0dc\uc2a4\ud0b9 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uac1d\uccb4 \uc704\uce58 \ucd94\uc815\uacfc \uc138\ubd80 \ubd84\ub958 \uc791\uc5c5\uc744 \uacb0\ud569\ud558\uc5ec \uc2e4\uc6a9\uc801\uc778 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \ub9cc\ub4dc\ub294 \ub370 \ucd08\uc810\uc744 \ub9de\ucda5\ub2c8\ub2e4.<\/p>\n
\ub2e8\uc77c \uc791\uc5c5\ub9cc \uc218\ud589\ud558\ub294 \ubaa8\ub378\ubcf4\ub2e4\ub294 \uc5ec\ub7ec \uc791\uc5c5\uc744 \ub3d9\uc2dc\uc5d0 \uc218\ud589\ud560 \uc218 \uc788\ub294 \uba40\ud2f0\ud0dc\uc2a4\ud0b9 \ubaa8\ub378\uc774 \ub354 \ud6a8\uc728\uc801\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc790\ub3d9\ucc28\ub97c \uc778\uc2dd\ud558\ub294 \ubaa8\ub378\uc774 \uc788\uc744 \ub54c, \uc774 \uc790\ub3d9\ucc28\uc758 \uc885\ub958(\uc138\ubd80 \ubd84\ub958)\uc640 \uc704\uce58(\uacbd\uacc4 \uc0c1\uc790)\ub97c \ub3d9\uc2dc\uc5d0 \uac10\uc9c0\ud560 \uc218 \uc788\ub2e4\uba74, \ub354 \uc801\uc740 \uc790\uc6d0\uc73c\ub85c \ub354 \ub9ce\uc740 \uc815\ubcf4\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
YOLO\ub294 \ud06c\uac8c 3\uac1c\uc758 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub85c \ub098\ub269\ub2c8\ub2e4:<\/p>\n
pip install tensorflow opencv-python matplotlib<\/code><\/pre>\n\uc704 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4. TensorFlow\ub294 YOLO \ubaa8\ub378\uc744 \uad6c\ud604\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uac83\uc774\uba70, OpenCV\ub294 \uc774\ubbf8\uc9c0\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc904 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
4. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n
YOLO \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \uc801\uc808\ud55c \ub370\uc774\ud130\uc14b\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. COCO \ub370\uc774\ud130\uc14b\uc774\ub098 PASCAL VOC \uac19\uc740 \uacf5\uac1c \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uc774\ubbf8\uc9c0\uc5d0\ub294 \uac1d\uccb4\uc758 \uacbd\uacc4 \uc0c1\uc790\uc640 \ud574\ub2f9 \ud074\ub798\uc2a4 \ub808\uc774\ube14\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ub370\uc774\ud130\uc14b\uc758 \uad6c\uc870<\/h3>\n
\ub370\uc774\ud130\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc870\ub85c \uc800\uc7a5\ub429\ub2c8\ub2e4:<\/p>\n
\n- images\/ (\uc774\ubbf8\uc9c0 \ud30c\uc77c)<\/li>\n
- labels\/ (\ub77c\ubca8 \ud30c\uc77c, \uac01 \uc774\ubbf8\uc9c0\ub2f9 \ud558\ub098\uc758 \ud14d\uc2a4\ud2b8 \ud30c\uc77c)<\/li>\n<\/ul>\n
5. YOLO \ubaa8\ub378 \uad6c\ud604<\/h2>\n
\ubaa8\ub378\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574 TensorFlow\uc758 Keras API\ub97c \ud65c\uc6a9\ud569\ub2c8\ub2e4. YOLO\ub97c \uc0ac\uc6a9\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\ub294 \ub2e4\uc591\ud55c \uc624\ud508\uc18c\uc2a4 \uad6c\ud604\uc774 \uc788\uc73c\ub098, \uc5ec\uae30\uc11c\ub294 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\ub97c \uc9c1\uc811 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport tensorflow as tf\n\ndef create_yolo_model(input_shape=(416, 416, 3)):\n inputs = tf.keras.Input(shape=input_shape)\n \n # Backbone Network (Feature Extraction)\n x = tf.keras.layers.Conv2D(32, (3, 3), strides=(2, 2), padding='same')(inputs)\n x = tf.keras.layers.BatchNormalization()(x)\n x = tf.keras.layers.LeakyReLU()(x)\n\n # Additional Layers...\n \n # Regression Head\n regression_output = tf.keras.layers.Conv2D(4, (1, 1), activation='sigmoid')(x) # x, y, w, h\n\n # Class Head\n class_output = tf.keras.layers.Conv2D(num_classes, (1, 1), activation='softmax')(x) # Class probabilities\n\n model = tf.keras.Model(inputs, [regression_output, class_output])\n return model\n\n# \ubaa8\ub378 \uc0dd\uc131\nyolo_model = create_yolo_model()\nyolo_model.summary()\n <\/code><\/pre>\n6. \ud559\uc2b5 \uacfc\uc815<\/h2>\n
YOLO \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud558\uace0, \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c YOLO\uc5d0\uc11c\ub294 \uc704\uce58 \ucd94\uc815\uacfc \ud074\ub798\uc2a4 \ubd84\ub958\uc5d0 \ub300\ud55c \uc190\uc2e4 \ud568\uc218\ub97c \uacb0\ud569\ud569\ub2c8\ub2e4.<\/p>\n
\ndef custom_loss(y_true, y_pred):\n # y_true: \uc2e4\uc81c\uac12 (\uc704\uce58, \ud074\ub798\uc2a4)\n # y_pred: \uc608\uce21\uac12 (\uc704\uce58, \ud074\ub798\uc2a4)\n\n # \uc190\uc2e4 \uacc4\uc0b0\n loss = tf.reduce_mean(tf.square(y_true - y_pred)) # \uc704\uce58 \uc190\uc2e4\n return loss\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nyolo_model.compile(optimizer='adam', loss=[custom_loss, 'sparse_categorical_crossentropy'])\n <\/code><\/pre>\n7. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud558\uace0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/p>\n
\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\n# train_images, train_labels = load_dataset()\n\n# \ubaa8\ub378 \ud559\uc2b5\nyolo_model.fit(train_images, train_labels, epochs=50, batch_size=16)\n <\/code><\/pre>\n8. \uacb0\uacfc \ud3c9\uac00 \ubc0f \uc608\uce21<\/h2>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uace0 \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc608\uce21 \uacb0\uacfc\ub294 \uacbd\uacc4 \uc0c1\uc790\uc640 \ud074\ub798\uc2a4 \ub808\uc774\ube14\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n
\ndef predict_image(image):\n image = preprocess(image) # \uc774\ubbf8\uc9c0 \uc804\ucc98\ub9ac\n pred_bbox, pred_class = yolo_model.predict(image)\n return pred_bbox, pred_class\n\n# \uc608\uce21 \uc218\ud589\npred_bbox, pred_class = predict_image(test_image)\n <\/code><\/pre>\n9. \uc2e4\uc6a9\uc801\uc778 \uc560\ud50c\ub9ac\ucf00\uc774\uc158 \ub9cc\ub4e4\uae30<\/h2>\n
\uc774\uc81c YOLO \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc2e4\uc6a9\uc801\uc778 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc2e4\uc2dc\uac04 \uac1d\uccb4 \ucd94\uc801, \uc790\uc728\uc8fc\ud589 \uc790\ub3d9\ucc28, \ubcf4\uc548 \uac10\uc2dc \uc2dc\uc2a4\ud15c \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
9.1 \uc2e4\uc2dc\uac04 \uac1d\uccb4 \uac10\uc9c0 \uc608\uc81c<\/h3>\n\nimport cv2\n\n# \ube44\ub514\uc624 \ucea1\ucc98\ncap = cv2.VideoCapture(0)\n\nwhile True:\n ret, frame = cap.read()\n if not ret:\n break\n \n pred_bbox, pred_class = predict_image(frame)\n # \uacbd\uacc4 \uc0c1\uc790 \uadf8\ub9ac\uae30...\n \n cv2.imshow('YOLO Object Detection', frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()\n <\/code><\/pre>\n10. \uacb0\ub860<\/h2>\n
YOLO\ub97c \ud65c\uc6a9\ud55c \uba40\ud2f0\ud0dc\uc2a4\ud0b9 \ubaa8\ub378 \uad6c\ucd95\uc740 \ud6a8\uacfc\uc801\uc778 \uac1d\uccb4 \uac10\uc9c0 \ubc0f \ubd84\ub958 \uc791\uc5c5\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 YOLO\uc758 \uae30\ubcf8 \uc6d0\ub9ac\uc640 \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc2e4\uc81c \uc751\uc6a9\ud504\ub85c\uadf8\ub7a8\uc5d0 YOLO\ub97c \uc801\uc6a9\ud558\uc5ec \ub354 \ubc1c\uc804\ub41c \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uc138\uc694.<\/p>\n
\ucc38\uace0\uc790\ub8cc<\/h3>\n