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YOLOV5改进
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YOLOv5对于小目标检测效果不佳的原因之一是小目标样本尺寸较小，YOLOv5的下采样乘数较大。较深的特征图使得学习小目标的特征变得困难，因此本文提出添加小目标检测层来检测较浅的特征图。 具体流程如图5所示。YOLOv5原本只对最后三个C3层进行特征预测，但由于小目标在连续下采样的过程中丢失了特征信息，导致小目标检测效果不理想。因此，我们添加了一层特征预测。新增的预测层下采样次数更少，小目标分辨率更高，有助于模型学习小目标的特征。 1模型修改介绍在head中，增加了小目标的检测层Detect3
文件路径在yolov5-master\models路径下yolov5x.yaml # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license # Parameters nc: 80 # number of classes depth_multiple: 1.33 # model depth multiple width_multiple: 1.25 # layer channel multiple anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 v6.0 backbone backbone: # [from, number, module, args] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ] # YOLOv5 v6.0 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 23 (P5/32-large) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] 3根据k-means聚类生成自己的预选框的位置需要填入以下: 输入图像大小，input_shape = [640, 640]。 生成的框数量，anchors_num = 12 输入输出的地址。 # -------------------------------------------------------------------------------------------------------# # kmeans虽然会对数据集中的框进行聚类，但是很多数据集由于框的大小相近，聚类出来的9个框相差不大， # 这样的框反而不利于模型的训练。因为不同的特征层适合不同大小的先验框，shape越小的特征层适合越大的先验框 # 原始网络的先验框已经按大中小比例分配好了，不进行聚类也会有非常好的效果。 # -------------------------------------------------------------------------------------------------------# import glob import xml.etree.ElementTree as ET import matplotlib.pyplot as plt import numpy as np from tqdm import tqdm def cas_ratio(box, cluster): ratios_of_box_cluster = box / cluster ratios_of_cluster_box = cluster / box ratios = np.concatenate([ratios_of_box_cluster, ratios_of_cluster_box], axis=-1) return np.max(ratios, -1) def avg_ratio(box, cluster): return np.mean([np.min(cas_ratio(box[i], cluster)) for i in range(box.shape[0])]) def kmeans(box, k): # -------------------------------------------------------------# # 取出一共有多少框 # -------------------------------------------------------------# row = box.shape[0] # -------------------------------------------------------------# # 每个框各个点的位置 # -------------------------------------------------------------# distance = np.empty((row, k)) # -------------------------------------------------------------# # 最后的聚类位置 # -------------------------------------------------------------# last_clu = np.zeros((row,)) np.random.seed() # -------------------------------------------------------------# # 随机选5个当聚类中心 # -------------------------------------------------------------# cluster = box[np.random.choice(row, k, replace=False)] iter = 0 while True: # -------------------------------------------------------------# # 计算当前框和先验框的宽高比例 # -------------------------------------------------------------# for i in range(row): distance[i] = cas_ratio(box[i], cluster) # -------------------------------------------------------------# # 取出最小点 # -------------------------------------------------------------# near = np.argmin(distance, axis=1) if (last_clu == near).all(): break # -------------------------------------------------------------# # 求每一个类的中位点 # -------------------------------------------------------------# for j in range(k): cluster[j] = np.median( box[near == j], axis=0) last_clu = near if iter % 5 == 0: print('iter: {:d}. avg_ratio:{:.2f}'.format(iter, avg_ratio(box, cluster))) iter += 1 return cluster, near def load_data(path): data = [] # -------------------------------------------------------------# # 对于每一个xml都寻找box # -------------------------------------------------------------# for xml_file in tqdm(glob.glob('{}/*xml'.format(path))): tree = ET.parse(xml_file) height = int(tree.findtext('./size/height')) width = int(tree.findtext('./size/width')) if height |
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