竞赛 深度学习卫星遥感图像检测与识别 -opencv python 目标检测-CSDN博客

0 前言

优质竞赛项目系列今天要分享的是

**深度学习卫星遥感图像检测与识别 **

该项目较为新颖适合作为竞赛课题方向学长非常推荐

学长这里给一个题目综合评分(每项满分5分)

• 难度系数3分
• 工作量3分
• 创新点5分

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1 课题背景

近年来,世界各国大力发展航空航天事业,卫星图像的目标检测在各行各业的应用得到了快速的发展,特别是军事侦查、海洋船舶和渔业管理等领域。由于卫星图像中有价值的信息极少,卫星图像数据规模巨大,这迫切需要智能辅助工具帮助相关从业人员从卫星图像中高效获取精确直观的信息。
本文利用深度学习技术基于Yolov5算法框架实现卫星图像目标检测问题。

2 实现效果

实现效果如下可以看出对船只、飞机等识别效果还是很好的。

3 Yolov5算法

简介
下图所示为 YOLOv5 的网络结构图分为输入端BackboneNeck 和 Prediction 四个部分。其中
输入端包括 Mosaic 数据增强、自适应图片缩放、自适应锚框计算Backbone 包括 Focus 结构、CSP
结 构Neck 包 括 FPN+PAN 结 构Prediction 包 括GIOU_Loss 结构。

相关代码

​

class Yolo(object):
    def __init__(self, weights_file, verbose=True):
        self.verbose = verbose
        # detection params
        self.S = 7  # cell size
        self.B = 2  # boxes_per_cell
        self.classes = ["aeroplane", "bicycle", "bird", "boat", "bottle",
                        "bus", "car", "cat", "chair", "cow", "diningtable",
                        "dog", "horse", "motorbike", "person", "pottedplant",
                        "sheep", "sofa", "train","tvmonitor"]
        self.C = len(self.classes) # number of classes
        # offset for box center (top left point of each cell)
        self.x_offset = np.transpose(np.reshape(np.array([np.arange(self.S)]*self.S*self.B),
                                              [self.B, self.S, self.S]), [1, 2, 0])
        self.y_offset = np.transpose(self.x_offset, [1, 0, 2])

        self.threshold = 0.2  # confidence scores threhold
        self.iou_threshold = 0.4
        #  the maximum number of boxes to be selected by non max suppression
        self.max_output_size = 10

        self.sess = tf.Session()
        self._build_net()
        self._build_detector()
        self._load_weights(weights_file)

4 数据处理和训练

数据集
本项目使用 DOTA 数据集原数据集中待检测的目标如下

原数据集中的标签如下

图像分割和尺寸调整
YOLO 模型的图像输入尺寸是固定的由于原数据集中的图像尺寸不一我们将原数据集中的图像按目标分布的位置分割成一个个包含目标的子图并将每个子图尺寸调整为
1024×1024。分割前后的图像如所示。
分割前

分割后

模型训练
在 yolov5/ 目录运行 train.py 文件开始训练

​

python train.py --weight weights/yolov5s.pt --batch 16 --epochs 100 --cache

其中的参数说明

• weight使用的预训练权重这里示范使用的是 yolov5s 模型的预训练权重
• batchmini-batch 的大小这里使用 16
• epochs训练的迭代次数这里我们训练 100 个 epoch
• cache使用数据缓存加速训练进程

相关代码

​

#部分代码
def train(hyp, opt, device, tb_writer=None):
    logger.info(f'Hyperparameters {hyp}')
    log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve'  # logging directory
    wdir = log_dir / 'weights'  # weights directory
    os.makedirs(wdir, exist_ok=True)
    last = wdir / 'last.pt'
    best = wdir / 'best.pt'
    results_file = str(log_dir / 'results.txt')
    epochs, batch_size, total_batch_size, weights, rank = \
        opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank

    # Save run settings
    with open(log_dir / 'hyp.yaml', 'w') as f:
        yaml.dump(hyp, f, sort_keys=False)
    with open(log_dir / 'opt.yaml', 'w') as f:
        yaml.dump(vars(opt), f, sort_keys=False)

    # Configure
    cuda = device.type != 'cpu'
    init_seeds(2 + rank)
    with open(opt.data) as f:
        data_dict = yaml.load(f, Loader=yaml.FullLoader)  # data dict
    with torch_distributed_zero_first(rank):
        check_dataset(data_dict)  # check
    train_path = data_dict['train']
    test_path = data_dict['val']
    nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names'])  # number classes, names
    assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check

    # Model
    pretrained = weights.endswith('.pt')
    if pretrained:
        with torch_distributed_zero_first(rank):
            attempt_download(weights)  # download if not found locally
        ckpt = torch.load(weights, map_location=device)  # load checkpoint
        if 'anchors' in hyp and hyp['anchors']:
            ckpt['model'].yaml['anchors'] = round(hyp['anchors'])  # force autoanchor
        model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device)  # create
        exclude = ['anchor'] if opt.cfg else []  # exclude keys
        state_dict = ckpt['model'].float().state_dict()  # to FP32
        state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude)  # intersect
        model.load_state_dict(state_dict, strict=False)  # load
        logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
    else:
        model = Model(opt.cfg, ch=3, nc=nc).to(device)  # create

    # Freeze
    freeze = ['', ]  # parameter names to freeze (full or partial)
    if any(freeze):
        for k, v in model.named_parameters():
            if any(x in k for x in freeze):
                print('freezing %s' % k)
                v.requires_grad = False

    # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / total_batch_size), 1)  # accumulate loss before optimizing
    hyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decay

    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
    for k, v in model.named_parameters():
        v.requires_grad = True
        if '.bias' in k:
            pg2.append(v)  # biases
        elif '.weight' in k and '.bn' not in k:
            pg1.append(v)  # apply weight decay
        else:
            pg0.append(v)  # all else

​

训练开始时的日志信息

5 最后

更多资料, 项目分享

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