探索智慧农业精准除草，基于高精度YOLOv8全系列参数【n/s/m/l/x】模型开发构建农田作物场景下杂草作物分割检测识别分析系统

智慧农业是未来的一个新兴赛道，随着科技的普及与落地应用，会有更加广阔的发展空间，关于农田作物场景下的项目开发实践，在我们前面的博文中也有很堵相关的实践，单大都是偏向于目标检测方向的，感兴趣可以自行移步阅读即可：

《自建数据集，基于YOLOv7开发构建农田场景下杂草检测识别系统》

《轻量级目标检测模型实战——杂草检测》

《激光除草距离我们实际的农业生活还有多远，结合近期所见所感基于yolov8开发构建田间作物杂草检测识别系统》

《基于yolov5的农作物田间杂草检测识别系统》

《AI助力智慧农业，基于YOLOv3开发构建农田场景下的庄稼作物、田间杂草智能检测识别系统》

《AI助力智慧农业，基于YOLOv4开发构建不同参数量级农田场景下庄稼作物、杂草智能检测识别系统》

《AI助力智慧农业，基于YOLOv5全系列模型【n/s/m/l/x】开发构建不同参数量级农田场景下庄稼作物、杂草智能检测识别系统》

《AI助力智慧农业，基于YOLOv6最新版本模型开发构建不同参数量级农田场景下庄稼作物、杂草智能检测识别系统》

《AI助力智慧农业，基于YOLOv7【tiny/yolov7/yolov7x】开发构建不同参数量级农田场景下庄稼作物、杂草智能检测识别系统》

《AI助力智慧农业，基于YOLOv8全系列模型【n/s/m/l/x】开发构建不同参数量级的识别系统》

《AI助力智慧农业，基于DETR【DEtection TRansformer】模型开发构建田间作物场景下庄稼作物、杂草检测识别系统》

《助力智能化农田作物除草，基于轻量级YOLOv8n开发构建农田作物场景下玉米苗、杂草检测识别分析系统》

《助力智能化农田作物除草，基于YOLOv8全系列【n/s/m/l/x】参数模型开发构建农田作物场景下玉米苗、杂草检测识别分析系统》

《助力智能化农田作物除草，基于DETR(DEtection TRansformer）模型开发构建农田作物场景下玉米苗、杂草检测识别分析系统》

《助力智能化农田作物除草，基于YOLOv3全系列【yolov3tiny/yolov3/yolov3spp】参数模型开发构建农田作物场景下玉米苗、杂草检测识别分析系统》

《助力智能化农田作物除草，基于YOLOv5全系列【n/s/m/l/x】参数模型开发构建农田作物场景下玉米苗、杂草检测识别分析系统》

《助力智能化农田作物除草，基于YOLOv6全系列【n/s/m/l】参数模型开发构建农田作物场景下玉米苗、杂草检测识别分析系统》

《助力智能化农田作物除草，基于YOLOv7【tiny/l/x】不同系列参数模型开发构建农田作物场景下玉米苗、杂草检测识别分析系统》

还有一些这里就不再一一列举了。

本文的主要目的是想要基于YOLOv8全系列的参数模型来开发构建农田作物场景下的杂草作物分割检测识别系统，首先看下实例效果：

接下来简单看下数据集：

YOLOv8官方项目地址在这里，如下所示：

目前已经收获超过1.7w的star量了。官方提供的预训练模型如下所示：

Model	size (pixels)	mAPbox 50-95	mAPmask 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-seg	640	36.7	30.5	96.1	1.21	3.4	12.6
YOLOv8s-seg	640	44.6	36.8	155.7	1.47	11.8	42.6
YOLOv8m-seg	640	49.9	40.8	317.0	2.18	27.3	110.2
YOLOv8l-seg	640	52.3	42.6	572.4	2.79	46.0	220.5
YOLOv8x-seg	640	53.4	43.4	712.1	4.02	71.8	344.1

可以根据自己的需求进行选择使用即可。

训练数据配置文件如下：

# Dataset
path: ./dataset
train:- /data/dataset/images/train
val:- /data/dataset/images/test
test:- /data/dataset/images/test# Classes
names:0: crop1: weed

这里我们一共开发了全系列五款不同参数量级的模型，实验阶段保持相同的参数设置，等待所有模型训练完成之后，我们来对其各个指标进行对比可视化。

【Precision曲线】
精确率曲线（Precision-Recall Curve）是一种用于评估二分类模型在不同阈值下的精确率性能的可视化工具。它通过绘制不同阈值下的精确率和召回率之间的关系图来帮助我们了解模型在不同阈值下的表现。
精确率（Precision）是指被正确预测为正例的样本数占所有预测为正例的样本数的比例。召回率（Recall）是指被正确预测为正例的样本数占所有实际为正例的样本数的比例。
绘制精确率曲线的步骤如下：
使用不同的阈值将预测概率转换为二进制类别标签。通常，当预测概率大于阈值时，样本被分类为正例，否则分类为负例。
对于每个阈值，计算相应的精确率和召回率。
将每个阈值下的精确率和召回率绘制在同一个图表上，形成精确率曲线。
根据精确率曲线的形状和变化趋势，可以选择适当的阈值以达到所需的性能要求。
通过观察精确率曲线，我们可以根据需求确定最佳的阈值，以平衡精确率和召回率。较高的精确率意味着较少的误报，而较高的召回率则表示较少的漏报。根据具体的业务需求和成本权衡，可以在曲线上选择合适的操作点或阈值。
精确率曲线通常与召回率曲线（Recall Curve）一起使用，以提供更全面的分类器性能分析，并帮助评估和比较不同模型的性能。

【Recall曲线】
召回率曲线（Recall Curve）是一种用于评估二分类模型在不同阈值下的召回率性能的可视化工具。它通过绘制不同阈值下的召回率和对应的精确率之间的关系图来帮助我们了解模型在不同阈值下的表现。
召回率（Recall）是指被正确预测为正例的样本数占所有实际为正例的样本数的比例。召回率也被称为灵敏度（Sensitivity）或真正例率（True Positive Rate）。
绘制召回率曲线的步骤如下：
使用不同的阈值将预测概率转换为二进制类别标签。通常，当预测概率大于阈值时，样本被分类为正例，否则分类为负例。
对于每个阈值，计算相应的召回率和对应的精确率。
将每个阈值下的召回率和精确率绘制在同一个图表上，形成召回率曲线。
根据召回率曲线的形状和变化趋势，可以选择适当的阈值以达到所需的性能要求。
通过观察召回率曲线，我们可以根据需求确定最佳的阈值，以平衡召回率和精确率。较高的召回率表示较少的漏报，而较高的精确率意味着较少的误报。根据具体的业务需求和成本权衡，可以在曲线上选择合适的操作点或阈值。

【F1值曲线】
F1值曲线是一种用于评估二分类模型在不同阈值下的性能的可视化工具。它通过绘制不同阈值下的精确率（Precision）、召回率（Recall）和F1分数的关系图来帮助我们理解模型的整体性能。F1分数是精确率和召回率的调和平均值，它综合考虑了两者的性能指标。F1值曲线可以帮助我们确定在不同精确率和召回率之间找到一个平衡点，以选择最佳的阈值。

【mAP0.5】
mAP0.5，也被称为mAP@0.5或AP50，指的是当Intersection over Union（IoU）阈值为0.5时的平均精度（mean Average Precision）。IoU是一个用于衡量预测边界框与真实边界框之间重叠程度的指标，其值范围在0到1之间。当IoU值为0.5时，意味着预测框与真实框至少有50%的重叠部分。
在计算mAP0.5时，首先会为每个类别计算所有图片的AP（Average Precision），然后将所有类别的AP值求平均，得到mAP0.5。AP是Precision-Recall Curve曲线下面的面积，这个面积越大，说明AP的值越大，类别的检测精度就越高。
mAP0.5主要关注模型在IoU阈值为0.5时的性能，当mAP0.5的值很高时，说明算法能够准确检测到物体的位置，并且将其与真实标注框的IoU值超过了阈值0.5。

【mAP0.5:0.95】
mAP0.5:0.95，也被称为mAP@[0.5:0.95]或AP@[0.5:0.95]，表示在IoU阈值从0.5到0.95变化时，取各个阈值对应的mAP的平均值。具体来说，它会在IoU阈值从0.5开始，以0.05为步长，逐步增加到0.95，并在每个阈值下计算mAP，然后将这些mAP值求平均。
这个指标考虑了多个IoU阈值下的平均精度，从而更全面、更准确地评估模型性能。当mAP0.5:0.95的值很高时，说明算法在不同阈值下的检测结果均非常准确，覆盖面广，可以适应不同的场景和应用需求。
对于一些需求比较高的场合，比如安全监控等领域，需要保证高的准确率和召回率，这时mAP0.5:0.95可能更适合作为模型的评价标准。
综上所述，mAP0.5和mAP0.5:0.95都是用于评估目标检测模型性能的重要指标，但它们的关注点有所不同。mAP0.5主要关注模型在IoU阈值为0.5时的性能，而mAP0.5:0.95则考虑了多个IoU阈值下的平均精度，从而更全面、更准确地评估模型性能。

【loss】

综合对比来看，x系列的模型效果反而不好，这个跟我们数据集过于小也有很大的关系。接下来为了依次对比不同系列的模型详细情况，我们分开每个指标下面每个模型进行可视化，如下：

【F1值】

【loss】

【mAP0.5】

【mAP0.5:0.95】

【Precision】

【Recall】

Batch实例如下：

混淆矩阵如下：

训练可视化如下：

离线推理实例如下：

掩模如下：

提取得到的边界数据如下：

{"weed": [[[922, 592, 1001, 692], [[947, 596], [945, 598], [939, 598], [935, 602], [935, 604], [933, 606], [933, 608], [927, 614], [927, 616], [925, 618], [925, 620], [923, 622], [923, 655], [925, 657], [925, 659], [931, 665], [933, 665], [937, 669], [939, 669], [951, 681], [953, 681], [955, 683], [957, 683], [959, 685], [961, 685], [963, 687], [965, 687], [967, 689], [978, 689], [982, 685], [984, 685], [986, 683], [988, 683], [994, 677], [994, 673], [996, 671], [996, 669], [1000, 665], [1000, 659], [1002, 657], [1002, 634], [1000, 632], [1000, 622], [996, 618], [996, 616], [994, 614], [994, 610], [986, 602], [986, 600], [984, 598], [982, 598], [980, 596]]], [[822, 460, 973, 632], [[919, 466], [917, 468], [911, 468], [909, 470], [907, 470], [905, 472], [903, 472], [901, 474], [899, 474], [897, 476], [893, 476], [891, 478], [888, 478], [886, 480], [878, 480], [876, 483], [872, 483], [870, 485], [866, 485], [864, 487], [862, 487], [860, 489], [858, 489], [856, 491], [854, 491], [852, 493], [850, 493], [846, 497], [844, 497], [842, 499], [840, 499], [836, 503], [834, 503], [828, 509], [828, 511], [826, 513], [826, 551], [828, 553], [828, 568], [830, 570], [830, 584], [832, 586], [832, 594], [834, 596], [834, 600], [836, 602], [836, 604], [840, 608], [840, 610], [842, 612], [842, 614], [852, 624], [854, 624], [856, 626], [858, 626], [860, 628], [864, 628], [866, 626], [872, 626], [874, 624], [876, 624], [878, 622], [880, 622], [882, 620], [884, 620], [886, 618], [888, 618], [893, 614], [895, 614], [901, 608], [903, 608], [907, 604], [909, 604], [911, 602], [913, 602], [915, 600], [917, 600], [919, 598], [921, 598], [925, 594], [927, 594], [929, 592], [933, 592], [935, 590], [931, 586], [931, 570], [933, 568], [933, 566], [953, 545], [953, 543], [955, 541], [955, 539], [957, 537], [957, 533], [959, 531], [959, 529], [961, 527], [961, 523], [963, 521], [963, 517], [965, 515], [965, 511], [967, 509], [967, 505], [969, 503], [969, 478], [967, 476], [967, 474], [965, 472], [965, 470], [963, 468], [961, 468], [959, 466]]], [[133, 356, 609, 821], [[301, 365], [299, 367], [291, 367], [289, 369], [285, 369], [283, 371], [281, 371], [279, 373], [277, 373], [271, 379], [271, 381], [251, 402], [249, 402], [216, 434], [214, 434], [208, 440], [208, 442], [198, 452], [198, 454], [192, 460], [192, 462], [184, 470], [184, 472], [180, 476], [180, 478], [176, 483], [176, 485], [164, 497], [162, 497], [149, 509], [149, 511], [143, 517], [143, 519], [141, 521], [141, 523], [139, 525], [139, 543], [137, 545], [137, 634], [139, 636], [139, 655], [141, 657], [141, 663], [145, 667], [145, 669], [147, 671], [147, 673], [149, 675], [149, 679], [151, 681], [151, 693], [153, 695], [151, 697], [151, 701], [149, 703], [149, 713], [151, 715], [151, 723], [153, 726], [153, 738], [155, 740], [155, 744], [157, 746], [157, 748], [176, 766], [178, 766], [180, 768], [182, 768], [188, 774], [190, 774], [194, 778], [196, 778], [198, 780], [200, 780], [210, 790], [212, 790], [220, 798], [220, 800], [232, 813], [234, 813], [236, 815], [238, 815], [240, 817], [273, 817], [275, 815], [291, 815], [293, 817], [309, 817], [311, 819], [334, 819], [336, 817], [344, 817], [352, 809], [354, 809], [360, 802], [360, 800], [362, 798], [364, 798], [368, 794], [370, 794], [374, 790], [376, 790], [382, 784], [384, 784], [386, 782], [388, 782], [390, 780], [392, 780], [407, 766], [409, 766], [415, 760], [417, 760], [419, 758], [421, 758], [425, 754], [427, 754], [431, 750], [433, 750], [435, 748], [437, 748], [439, 746], [441, 746], [445, 742], [447, 742], [451, 738], [453, 738], [455, 736], [459, 736], [461, 734], [465, 734], [467, 732], [469, 732], [471, 730], [475, 730], [477, 728], [483, 728], [486, 726], [490, 726], [492, 723], [494, 723], [496, 721], [498, 721], [502, 717], [504, 717], [510, 711], [512, 711], [532, 691], [532, 689], [556, 665], [556, 663], [558, 661], [558, 659], [562, 655], [562, 653], [564, 651], [564, 649], [567, 647], [567, 642], [569, 640], [569, 638], [571, 636], [571, 634], [573, 632], [573, 630], [575, 628], [575, 626], [577, 624], [577, 622], [581, 618], [581, 614], [583, 612], [583, 608], [585, 606], [585, 602], [587, 600], [587, 596], [589, 594], [589, 588], [591, 586], [591, 576], [593, 574], [593, 566], [595, 564], [595, 561], [597, 559], [597, 557], [601, 553], [601, 551], [603, 549], [603, 547], [605, 545], [605, 543], [607, 541], [607, 537], [609, 535], [609, 505], [607, 503], [607, 499], [605, 497], [605, 495], [603, 493], [603, 491], [599, 487], [599, 485], [597, 483], [597, 480], [595, 478], [595, 476], [589, 470], [589, 468], [585, 464], [585, 462], [573, 450], [573, 448], [556, 432], [556, 430], [548, 422], [548, 420], [544, 416], [544, 414], [540, 410], [540, 408], [534, 402], [534, 399], [526, 391], [526, 389], [514, 377], [512, 377], [508, 373], [506, 373], [502, 369], [500, 369], [498, 367], [496, 367], [494, 365], [453, 365], [451, 367], [447, 367], [445, 369], [443, 369], [441, 371], [439, 371], [427, 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感兴趣的话也都可以自行动手尝试一下吧！