Salta el contingut

Postprocessament

En models de detecció d’objectes com YOLO, el postprocessament és essencial per refinar les prediccions en brut fetes per la xarxa. Com hem vist a la Lliçó 2, aquests models tendeixen a produir un gran nombre de prediccions.

Aquest comportament es deu a l’arquitectura del model, on:

  • Cada cel·la de la graella prediu múltiples anchor boxes.
  • Les cel·les veïnes poden detectar el mateix objecte.

Com a resultat, diverses caixes superposades poden representar el mateix objecte. Si mantenim totes les caixes predites, la sortida contindria molts duplicats.

Per abordar-ho, el postprocessament inclou:

  • Llindarització per confiança: filtra les deteccions de baixa confiança.
  • Non-Maximum Suppression (NMS): elimina caixes superposades redundants i manté només la de més confiança.

Aquest pas garanteix resultats més nets i precisos.

L’objectiu d’aquesta lliçó és aprendre com implementar filtres de postprocessament i entendre com ens poden ajudar a millorar els resultats.

Afegir la ruta del mòdul d’ajuda

Primer, afegim el mòdul d’ajuda:

addpath('help-module');

Carregar un model

A continuació, carreguem el model al qual aplicarem les tècniques de postprocessament:

modelPath = fullfile(pwd, 'runs', 'detect', 'train10', 'weights');
configFile = fullfile(pwd, 'datasets', 'fruits_3_4998', 'data.yaml');

yolov8Det = utils.loadModel(modelPath, configFile);

Exercici 1 - Filtre de confiança

Implementa un filtre de confiança que elimini les deteccions amb un nivell de confiança inferior al llindar.

function filteredDetections  = filterByConfidence(detections, threshold)
    % Elimina les deteccions amb confiança per sota d’un llindar.
    % Inputs:
    %   detections - Array d’estructures, cadascuna representant un resultat de detecció amb camps:
    %                .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                .conf  (Nx1): puntuacions de confiança
    %                .cls   (Nx1): etiquetes de classe
    %   threshold  - Valor escalar que especifica la puntuació mínima de confiança necessària per mantenir una detecció
    %
    % Output:
    %   filteredDetections - Array d’estructures amb les deteccions de baixa confiança eliminades.

end

Solució

function filteredDetections  = filterByConfidence(detections, threshold)
    % Elimina les deteccions amb confiança per sota d’un llindar.
    % Inputs:
    %   detections - Array d’estructures, cadascuna representant un resultat de detecció amb camps:
    %                .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                .conf  (Nx1): puntuacions de confiança
    %                .cls   (Nx1): etiquetes de classe
    %   threshold  - Valor escalar que especifica la puntuació mínima de confiança necessària per mantenir una detecció
    %
    % Output:
    %   filteredDetections - Array d’estructures amb les deteccions de baixa confiança eliminades.

    n = numel(detections);
    filteredDetections = [];
    for i = 1:n
        detection = detections(i);
        conf = detection.conf;
        keep = conf >= threshold;
        if keep
           filteredDetections = [filteredDetections, detection];
        else
            fprintf('Filtered: removed low-confidence detection\n');
        end
    end
end

En el codi següent, definim una funció anònima per al filtre que hem implementat en l’exercici anterior, que després utilitzem per crear un component de postprocessament.

ScoreThreshold = 0.73;

removeLowScore = @(detections) filterByConfidence(detections, ScoreThreshold);
postprocessing = utils.PostProcessing(yolov8Det, {removeLowScore});

Aquest component de postprocessament s’utilitza en el codi següent per aplicar el nostre filtre a les deteccions, que podem controlar utilitzant la variable ScoreThreshold.

utils.displayFilteredDetections(yolov8Det, 'pear.jpg', postprocessing);

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Exercici 2 - Filtre de confiança per classes

Com en l’exercici anterior, has d’implementar un filtre de confiança que elimini les deteccions amb una puntuació de confiança inferior a un llindar. Tanmateix, aquesta vegada cada classe ha de tenir el seu propi llindar personalitzat.

function filteredDetection = filterByClassConfidence(detection, thresholds)
   % Filtra les deteccions utilitzant un llindar de confiança específic per a cada classe.
    % Inputs:
    %   detections  - Array d’estructures, cadascuna representant resultats de detecció amb camps:
    %                 .boxes (Nx4): bounding boxes
    %                 .conf  (Nx1): puntuacions de confiança
    %                 .cls   (Nx1): etiquetes de classe (com a enters que comencen per 1)
    %
    %   thresholds  - Vector de llindars de confiança específics per classe.
    %                 thresholds(i) és la confiança mínima necessària per a la classe i.
    %
    % Output:
    %   filteredDetections - Array d’estructures amb les deteccions de baixa confiança eliminades,
    %                        on el filtratge s’aplica per classe utilitzant els llindars corresponents.

end 

function filteredDetections = filterByClassConfidence(detections, thresholds)
    % Filtra les deteccions utilitzant un llindar de confiança específic per a cada classe.
    % Inputs:
    %   detections  - Array d’estructures, cadascuna representant resultats de detecció amb camps:
    %                 .boxes (Nx4): bounding boxes
    %                 .conf  (Nx1): puntuacions de confiança
    %                 .cls   (Nx1): etiquetes de classe (com a enters que comencen per 1)
    %
    %   thresholds  - Vector de llindars de confiança específics per classe.
    %                 thresholds(i) és la confiança mínima necessària per a la classe i.
    %
    % Output:
    %   filteredDetections - Array d’estructures amb les deteccions de baixa confiança eliminades,
    %                        on el filtratge s’aplica per classe utilitzant els llindars corresponents.

    n = numel(detections);
    filteredDetections = [];  % preassigna l’array d’estructures
    for i = 1:n
        detection = detections(i);

        cls = detection.cls;
        conf = detection.conf;
        keep = conf >= thresholds(cls);

        if keep
            filteredDetections = [filteredDetections, detection];
        else
            fprintf('Filtered: removed low-confidence detections (per class)\n');
        end
    end
end 

% Vector de llindars per classe (índex = classe)
appleThreshold = 0.66;
orangeThreshold = 0.45;
pearThreshold = 0.61;

scoreThresholds = [appleThreshold, orangeThreshold, pearThreshold];
removeLowScorePerClass = @(detections) filterByClassConfidence(detections, scoreThresholds)

removeLowScorePerClass = function_handle with value:
    @(detections)filterByClassConfidence(detections,scoreThresholds)

postprocessing = utils.PostProcessing(yolov8Det, {removeLowScorePerClass});

utils.displayFilteredDetections(yolov8Det, 'multiple.jpg', postprocessing);

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Intersection over Union (IoU)

En detecció d’objectes, un concepte fonamental és Intersection over Union (IoU). Aquesta mètrica quantifica la superposició entre dues bounding boxes: la caixa predita pel model i la caixa ground truth. La IoU s’utilitza per avaluar com de precisa és una predicció en relació amb l’anotació real.

Formalment, la IoU es defineix com la relació entre l’àrea d’intersecció i l’àrea d’unió de les dues caixes:

image_0.png

El valor de la IoU està limitat entre 0 i 1:

  • IoU = 0 vol dir que no hi ha superposició.
  • IoU = 1 indica una coincidència perfecta entre la predicció i el ground truth.

Exercici 3 - Calcular IoU

Implementa una funció que calculi la Intersection over Union de dues bounding boxes:

Pista

$$ \textrm{union}={\textrm{area}}_1 +{\textrm{area}}_2 -\textrm{intersection} $$ 

function iou = calculateIoU(b1, b2)
    % Calcula la Intersection over Union (IoU) entre dues bounding boxes.
    %
    % Inputs:
    %   b1 - Vector 1x4 que representa la primera bounding box [x1 y1 x2 y2]
    %   b2 - Vector 1x4 que representa la segona bounding box [x1 y1 x2 y2]
    %
    % Output:
    %   iou - Valor escalar entre 0 i 1 que representa la IoU.
    %         IoU = 0 si no hi ha superposició.
    %         IoU = 1 si les caixes són idèntiques.

    iou = calculateIoUSolution(b1, b2);

end


function iou = calculateIoU(b1, b2)
    % Calcula la Intersection over Union (IoU) entre dues bounding boxes.
    %
    % Inputs:
    %   b1 - Vector 1x4 que representa la primera bounding box [x1 y1 x2 y2]
    %   b2 - Vector 1x4 que representa la segona bounding box [x1 y1 x2 y2]
    %
    % Output:
    %   iou - Valor escalar entre 0 i 1 que representa la IoU.
    %         IoU = 0 si no hi ha superposició.
    %         IoU = 1 si les caixes són idèntiques.

    x1 = max(b1(1), b2(1));
    y1 = max(b1(2), b2(2));
    x2 = min(b1(3), b2(3));
    y2 = min(b1(4), b2(4));
    inter = max(0, x2-x1) * max(0, y2-y1);
    area1 = (b1(3)-b1(1))*(b1(4)-b1(2));
    area2 = (b2(3)-b2(1))*(b2(4)-b2(2));
    union = area1 + area2 - inter;
    if union>0
        iou = inter/union;
    else
        iou = 0;
    end

end

Test

utils.displayIoU([0 0 2 2], [2 2 4 4], @calculateIoU) % expected 0

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utils.displayIoU([2 2 4 4], [2 2 4 4], @calculateIoU) % expected 1

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utils.displayIoU([0 0 2 2], [1 1 3 3], @calculateIoU) % expected 0.1429

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utils.displayIoU([0 0 2 2], [1 1 2 2], @calculateIoU) % expected 0.25

figure_5.png

utils.displayIoU([0 0 3 3], [0.5 0 3 3], @calculateIoU); % Expectected 0.8333

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Exercici 4 - Non-Maximum Suppression

En aquest exercici, el teu objectiu és implementar Non-Maximum Suppression (NMS) per eliminar deteccions superposades redundants de la mateixa classe.

Com funciona NMS

L’algorisme bàsic de NMS consta d’aquests passos:

  1. Filtrar per puntuació de confiança: elimina totes les caixes amb confiança (objectness × probabilitat de classe) per sota d’un llindar, per exemple 0.25.
  2. Ordenar les caixes per puntuació: ordena les caixes restants en ordre descendent segons les seves puntuacions de confiança.

  3. Suprimir caixes superposades:

  4. Agafa la caixa amb la confiança més alta, anomenem-la B1.

  5. Calcula la IoU (Intersection over Union) de B1 amb totes les altres caixes:IoU
  6. IoUThreshold (per exemple, 0.5), descarta aquestes caixes, ja que probablement representen el mateix objecte.
  7. Repeteix amb la següent caixa restant.

Pista

Utilitza la funció següent per convertir bounding boxes al format requerit per la funció calculateIoU:

function boxXYXY = xywh2xyxy(boxXYWH)
    % Converteix [x y w h] a [x1 y1 x2 y2]
    boxXYXY = [boxXYWH(1), ...
               boxXYWH(2), ...
               boxXYWH(1) + boxXYWH(3), ...
               boxXYWH(2) + boxXYWH(4)];
end

function filteredDetections = filterByOverlapping(detections, iouThreshold, calculateIoU)
    % Elimina deteccions superposades basant-se en Intersection over Union (IoU).
    % Inputs:
    %   detections    - Array d’estructures que conté resultats de detecció amb camps:
    %                  .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                  .conf  (Nx1): puntuacions de confiança
    %                  .cls   (Nx1): etiquetes de classe
    %   iouThreshold - Llindar escalar per a la IoU. Les deteccions amb IoU per sobre d’aquest 
    %                  valor (i de la mateixa classe) es consideren duplicats.
    %   calculateIoU  - Function handle per calcular la IoU entre dues caixes.
    %
    % Output:
    %   filteredDetection - Estructura de detecció amb les caixes superposades eliminades,
    %                       mantenint la de confiança més alta.
end

function filteredDetections = filterByOverlapping(detections, iouThreshold, calculateIoU)
    % Elimina deteccions superposades basant-se en Intersection over Union (IoU).
    % Inputs:
    %   detections    - Array d’estructures que conté resultats de detecció amb camps:
    %                  .boxes (Nx4): bounding boxes [x1 y1 x2 y2]
    %                  .conf  (Nx1): puntuacions de confiança
    %                  .cls   (Nx1): etiquetes de classe
    %   iouThreshold - Llindar escalar per a la IoU. Les deteccions amb IoU per sobre d’aquest 
    %                  valor (i de la mateixa classe) es consideren duplicats.
    %   calculateIoU  - Function handle per calcular la IoU entre dues caixes.
    %
    % Output:
    %   filteredDetection - Estructura de detecció amb les caixes superposades eliminades,
    %                       mantenint la de confiança més alta.


    % Extreu els components individuals de la detecció
    boxes = vertcat(detections.boxes);  % Nx4
    conf  = vertcat(detections.conf);   % Nx1
    cls   = vertcat(detections.cls);    % Nx1

    N = size(boxes, 1);         % Nombre total de deteccions

    % Ordena les deteccions per confiança descendent
    [~, order] = sort(conf, 'descend');

    keep = false(N,1);          % Màscara booleana per fer el seguiment de quines caixes es mantenen

    % Itera sobre les deteccions en ordre ordenat
    for k = 1:N
        idx = order(k);         % Índex de la detecció actual
        keepThis = true;        % Flag que indica si es manté la caixa actual

        % Compara amb les caixes mantingudes prèviament
        for j = find(keep)'     % Índexs de les caixes ja mantingudes
            if cls(idx) == cls(j)   % Només compara caixes de la mateixa classe
                iou = calculateIoU(xywh2xyxy(boxes(idx,:)), xywh2xyxy(boxes(j,:)));
                if iou > iouThreshold
                    keepThis = false;
                    break;
                end
            end
        end

        if keepThis
            keep(idx) = true;
        end
    end

    % Manté només les deteccions seleccionades
    filteredDetections.boxes = boxes(keep,:);
    filteredDetections.conf  = conf(keep);
    filteredDetections.cls   = cls(keep);

    removed = sum(~keep);
    if removed > 0
        fprintf('Filtered: removed %d overlapping detections\n', removed);
    end
end

ScoreThreshold = 0.5;
removeLowScore = @(detections) filterByConfidence(detections, ScoreThreshold);

iouThreshold = 0.5;
removeOverlapping = @(detections) filterByOverlapping(detections, iouThreshold, @calculateIoU);

filters = {removeLowScore, removeOverlapping};
postprocessing = utils.PostProcessing(yolov8Det, filters);
utils.displayFilteredDetections(yolov8Det, 'pear.jpg', postprocessing);

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figure_7.png

utils.displayFilteredDetections(yolov8Det, 'orange.jpg', postprocessing);

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figure_8.png

utils.displayFilteredDetections(yolov8Det, 'orange2.jpg', postprocessing);

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figure_9.png

utils.displayFilteredDetections(yolov8Det, 'apple.jpg', postprocessing);

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figure_10.png

utils.displayFilteredDetections(yolov8Det, 'apple2.jpg', postprocessing);

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figure_11.png

utils.displayFilteredDetections(yolov8Det, 'multiple.jpg', postprocessing);

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Filtered: removed low-confidence detection
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Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed low-confidence detection
Filtered: removed 36 overlapping detections

figure_12.png

Ara, després de filtrar les prediccions, obtenim bons resultats; tanmateix, no tenim cap idea de com de bo és realment el model.

En la pròxima lliçó, abordarem aquesta qüestió.