YOLOv8-obb with TensorRT backed

README.md

@@ -3,6 +3,8 @@
 
 # Last changes
 
+* YOLOv8-obb detector worked with TensorRT! Export pretrained Pytorch models [here (ultralytics/ultralytics)](https://github.com/ultralytics/ultralytics) to onnx format and run Multitarget-tracker with -e=6 example
+
 * YOLOv10 detector worked with TensorRT! Export pretrained Pytorch models [here (THU-MIG/yolov10)](https://github.com/THU-MIG/yolov10) to onnx format and run Multitarget-tracker with -e=6 example
 
 * YOLOv9 detector worked with TensorRT! Export pretrained Pytorch models [here (WongKinYiu/yolov9)](https://github.com/WongKinYiu/yolov9) to onnx format and run Multitarget-tracker with -e=6 example
@@ -13,6 +15,11 @@
 
 # New videos!
 
+* YOLOv8-obb detection with rotated boxes (DOTA v1.0 trained)
+
+[![YOLOv8-obb detection:](https://img.youtube.com/vi/1e6ur57Fhzs/0.jpg)](https://youtu.be/1e6ur57Fhzs)
+
+
 * YOLOv7 instance segmentation
 
 [![YOLOv7 instance segmentation:](https://img.youtube.com/vi/gZxuYyFz1dU/0.jpg)](https://youtu.be/gZxuYyFz1dU)

combined/combined.cpp

@@ -328,6 +328,7 @@ bool CombinedDetector::InitDetector(cv::UMat frame)
 		YOLOv7Mask,
 		YOLOv8,
 		YOLOv8Mask,
+		YOLOV8_OBB,
 		YOLOv9
 	};
 	YOLOModels usedModel = YOLOModels::YOLOv8;

data/DOTA.names

@@ -0,0 +1,15 @@
+plane
+ship
+storage_tank
+baseball_diamond
+tennis_court
+basketball_court
+ground_track_field
+harbor
+bridge
+large_vehicle
+small_vehicle
+helicopter
+roundabout
+soccer_ball_field
+swimming_pool

data/coco/full.names

@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+pottedplant
+bed
+diningtable
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

data/settings_yolov10.ini

@@ -0,0 +1,142 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 12
+# darknet_cudnn = 10
+# tensorrt = 11
+detector_backend = 12
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov10s.onnx
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov10s.onnx
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
+
+#-----------------------------
+confidence_threshold = 0.3
+
+max_crop_ratio = 0
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3 
+# YOLOV4 
+# YOLOV5 
+net_type = YOLOV10
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP16
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0   // Euclidean distance between centers, pixels
+# DistRects = 1     // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2   // Intersection over Union, IoU, [0, 1]
+# DistHist = 3      // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+# FilterRRect = 2
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# TrackDaSiamRPN = 10
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8 
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this frames then it will be removed
+max_skip_frames = 50
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 50
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10